Articles | Volume 21, issue 6
https://doi.org/10.5194/nhess-21-1825-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-21-1825-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
15 Jun 2021
Review article |
| 15 Jun 2021
Review article: Detection of actionable tweets in crisis events
Technical University of Munich, Data Science in Earth Observation, Munich, Germany
Jens Kersten
German Aerospace Center, Institute of Data Science, Jena, Germany
Friederike Klan
German Aerospace Center, Institute of Data Science, Jena, Germany
Related authors
No articles found.
Matti Wiegmann, Jens Kersten, Hansi Senaratne, Martin Potthast, Friederike Klan, and Benno Stein
Nat. Hazards Earth Syst. Sci., 21, 1431–1444, https://doi.org/10.5194/nhess-21-1431-2021, https://doi.org/10.5194/nhess-21-1431-2021, 2021
Short summary
Short summary
In this paper, we study when social media is an adequate source to find metadata about incidents that cannot be acquired by traditional means. We identify six major use cases: impact assessment and verification of model predictions, narrative generation, recruiting citizen volunteers, supporting weakly institutionalized areas, narrowing surveillance areas, and reporting triggers for periodical surveillance.
This article is included in the Encyclopedia of Geosciences
Related subject area
Databases, GIS, Remote Sensing, Early Warning Systems and Monitoring Technologies
A neural network model for automated prediction of avalanche danger level
Brief communication: Landslide activity on the Argentinian Santa Cruz River mega dam works confirmed by PSI DInSAR
Inform@Risk. The Development of a Prototype for an Integrated Landslide Early Warning System in an Informal Settlement: the Case of Bello Oriente in Medellín, Colombia
Impact of topography on in situ soil wetness measurements for regional landslide early warning – a case study from the Swiss Alpine Foreland
Earthquake building damage detection based on synthetic-aperture-radar imagery and machine learning
Assessing riverbank erosion in Bangladesh using time series of Sentinel-1 radar imagery in the Google Earth Engine
Quantifying unequal urban resilience to rainfall across China from location-aware big data
Comparison of machine learning techniques for reservoir outflow forecasting
Development of black ice prediction model using GIS-based multi-sensor model validation
Machine learning nowcasting of the Vögelsberg deep-seated landslide: why predicting slow deformation is not so easy
Forecasting vegetation condition with a Bayesian auto-regressive distributed lags (BARDL) model
A dynamic hierarchical Bayesian approach for forecasting vegetation condition
Using a single remote-sensing image to calculate the height of a landslide dam and the maximum volume of a lake
Enhancing disaster risk resilience using greenspace in urbanising Quito, Ecuador
Gridded flood depth estimates from satellite-derived inundations
ProbFire: a probabilistic fire early warning system for Indonesia
Index establishment and capability evaluation of space–air–ground remote sensing cooperation in geohazard emergency response
Brief communication: Monitoring a soft-rock coastal cliff using webcams and strain sensors
Multiscale analysis of surface roughness for the improvement of natural hazard modelling
EUNADICS-AV early warning system dedicated to supporting aviation in the case of a crisis from natural airborne hazards and radionuclide clouds
Are sirens effective tools to alert the population in France?
UAV survey method to monitor and analyze geological hazards: the case study of the mud volcano of Villaggio Santa Barbara, Caltanissetta (Sicily)
Timely prediction potential of landslide early warning systems with multispectral remote sensing: a conceptual approach tested in the Sattelkar, Austria
CHILDA – Czech Historical Landslide Database
Long-term magnetic anomalies and their possible relationship to the latest greater Chilean earthquakes in the context of the seismo-electromagnetic theory
HazMapper: a global open-source natural hazard mapping application in Google Earth Engine
Opportunities and risks of disaster data from social media: a systematic review of incident information
Online urban-waterlogging monitoring based on a recurrent neural network for classification of microblogging text
Predicting power outages caused by extratropical storms
Near-real-time automated classification of seismic signals of slope failures with continuous random forests
Assessing the accuracy of remotely sensed fire datasets across the southwestern Mediterranean Basin
Responses to severe weather warnings and affective decision-making
The object-specific flood damage database HOWAS 21
A spaceborne SAR-based procedure to support the detection of landslides
GIS-based DRASTIC and composite DRASTIC indices for assessing groundwater vulnerability in the Baghin aquifer, Kerman, Iran
Review article: The spatial dimension in the assessment of urban socio-economic vulnerability related to geohazards
Design and implementation of a mobile device app for network-based earthquake early warning systems (EEWSs): application to the PRESTo EEWS in southern Italy
CCAF-DB: the Caribbean and Central American active fault database
Evaluation of a combined drought indicator and its potential for agricultural drought prediction in southern Spain
Study on real-time correction of site amplification factor
Three-dimensional rockfall shape back analysis: methods and implications
Effects of high-resolution geostationary satellite imagery on the predictability of tropical thunderstorms over Southeast Asia
InSAR technique applied to the monitoring of the Qinghai–Tibet Railway
Understanding the spatiotemporal development of human settlement in hurricane-prone areas on the US Atlantic and Gulf coasts using nighttime remote sensing
Pre-disaster mapping with drones: an urban case study in Victoria, British Columbia, Canada
New approaches to modelling of local seismic amplification susceptibility using direct characteristics of influencing criteria: case study of Bam City, Iran
SMC-Flood database: a high-resolution press database on flood cases for the Spanish Mediterranean coast (1960–2015)
Statistical analysis for satellite-index-based insurance to define damaged pasture thresholds
Monitoring the seasonal dynamics of soil salinization in the Yellow River delta of China using Landsat data
Towards early warning of gravitational slope failure with co-detection of microseismic activity: the case of an active rock glacier
Vipasana Sharma, Sushil Kumar, and Rama Sushil
Nat. Hazards Earth Syst. Sci., 23, 2523–2530, https://doi.org/10.5194/nhess-23-2523-2023, https://doi.org/10.5194/nhess-23-2523-2023, 2023
Short summary
Short summary
Snow avalanches are a natural hazard that can cause danger to human lives. This threat can be reduced by accurate prediction of the danger levels. The development of mathematical models based on past data and present conditions can help to improve the accuracy of prediction. This research aims to develop a neural-network-based model for correlating complex relationships between the meteorological variables and the profile variables.
This article is included in the Encyclopedia of Geosciences
Guillermo Tamburini-Beliveau, Sebastián Balbarani, and Oriol Monserrat
Nat. Hazards Earth Syst. Sci., 23, 1987–1999, https://doi.org/10.5194/nhess-23-1987-2023, https://doi.org/10.5194/nhess-23-1987-2023, 2023
Short summary
Short summary
Landslides and ground deformation associated with the construction of a hydropower mega dam in the Santa Cruz River in Argentine Patagonia have been monitored using radar and optical satellite data, together with the analysis of technical reports. This allowed us to assess the integrity of the construction, providing a new and independent dataset. We have been able to identify ground deformation trends that put the construction works at risk.
This article is included in the Encyclopedia of Geosciences
Christian Werthmann, Marta Sapena, Marlene Kühnl, John Singer, Carolina Garcia, Bettina Menschik, Heike Schäfer, Sebastian Schröck, Lisa Seiler, Kurosch Thuro, and Hannes Taubenböck
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-53, https://doi.org/10.5194/nhess-2023-53, 2023
Revised manuscript under review for NHESS
Short summary
Short summary
Early Warning Systems (EWS) promise to decrease the vulnerability of self-constructed (informal) settlements threatened by landslides. A living lab sought to develop an integrated EWS in a landslide exposed neighborhood in Medellín. Findings indicate that technical aspects can be manageable, lesser social and political dynamics. A resilient EWS for informal settlements has to achieve sufficient social and technical redundancy to maintain basic functionality in a reduced support scenario.
This article is included in the Encyclopedia of Geosciences
Adrian Wicki, Peter Lehmann, Christian Hauck, and Manfred Stähli
Nat. Hazards Earth Syst. Sci., 23, 1059–1077, https://doi.org/10.5194/nhess-23-1059-2023, https://doi.org/10.5194/nhess-23-1059-2023, 2023
Short summary
Short summary
Soil wetness measurements are used for shallow landslide prediction; however, existing sites are often located in flat terrain. Here, we assessed the ability of monitoring sites at flat locations to detect critically saturated conditions compared to if they were situated at a landslide-prone location. We found that differences exist but that both sites could equally well distinguish critical from non-critical conditions for shallow landslide triggering if relative changes are considered.
This article is included in the Encyclopedia of Geosciences
Anirudh Rao, Jungkyo Jung, Vitor Silva, Giuseppe Molinario, and Sang-Ho Yun
Nat. Hazards Earth Syst. Sci., 23, 789–807, https://doi.org/10.5194/nhess-23-789-2023, https://doi.org/10.5194/nhess-23-789-2023, 2023
Short summary
Short summary
This article presents a framework for semi-automated building damage assessment due to earthquakes from remote-sensing data and other supplementary datasets including high-resolution building inventories, while also leveraging recent advances in machine-learning algorithms. For three out of the four recent earthquakes studied, the machine-learning framework is able to identify over 50 % or nearly half of the damaged buildings successfully.
This article is included in the Encyclopedia of Geosciences
Jan Freihardt and Othmar Frey
Nat. Hazards Earth Syst. Sci., 23, 751–770, https://doi.org/10.5194/nhess-23-751-2023, https://doi.org/10.5194/nhess-23-751-2023, 2023
Short summary
Short summary
In Bangladesh, riverbank erosion occurs every year during the monsoon and affects thousands of households. Information on locations and extent of past erosion can help anticipate where erosion might occur in the upcoming monsoon season and to take preventive measures. In our study, we show how time series of radar satellite imagery can be used to retrieve information on past erosion events shortly after the monsoon season using a novel interactive online tool based on the Google Earth Engine.
This article is included in the Encyclopedia of Geosciences
Jiale Qian, Yunyan Du, Jiawei Yi, Fuyuan Liang, Nan Wang, Ting Ma, and Tao Pei
Nat. Hazards Earth Syst. Sci., 23, 317–328, https://doi.org/10.5194/nhess-23-317-2023, https://doi.org/10.5194/nhess-23-317-2023, 2023
Short summary
Short summary
Human activities across China show a similar trend in response to rains. However, urban resilience varies significantly by region. The northwestern arid region and the central underdeveloped areas are very fragile, and even low-intensity rains can trigger significant human activity anomalies. By contrast, even high-intensity rains might not affect residents in the southeast.
This article is included in the Encyclopedia of Geosciences
Orlando García-Feal, José González-Cao, Diego Fernández-Nóvoa, Gonzalo Astray Dopazo, and Moncho Gómez-Gesteira
Nat. Hazards Earth Syst. Sci., 22, 3859–3874, https://doi.org/10.5194/nhess-22-3859-2022, https://doi.org/10.5194/nhess-22-3859-2022, 2022
Short summary
Short summary
Extreme events have increased in the last few decades; having a good estimation of the outflow of a reservoir can be an advantage for water management or early warning systems. This study analyzes the efficiency of different machine learning techniques to predict reservoir outflow. The results obtained showed that the proposed models provided a good estimation of the outflow of the reservoirs, improving the results obtained with classical approaches.
This article is included in the Encyclopedia of Geosciences
Seok Bum Hong, Hong Sik Yun, Sang Guk Yum, Seung Yeop Ryu, In Seong Jeong, and Jisung Kim
Nat. Hazards Earth Syst. Sci., 22, 3435–3459, https://doi.org/10.5194/nhess-22-3435-2022, https://doi.org/10.5194/nhess-22-3435-2022, 2022
Short summary
Short summary
This study advances previous models through machine learning and multi-sensor-verified results. Using spatial and meteorological data from the study area (Suncheon–Wanju Highway in Gurye-gun), the amount and location of black ice were modelled based on system dynamics to predict black ice and then simulated with the geographic information system (m2). Based on the model results, multiple sensors were buried at four selected points in the study area, and the model was compared with sensor data.
This article is included in the Encyclopedia of Geosciences
Adriaan L. van Natijne, Thom A. Bogaard, Thomas Zieher, Jan Pfeiffer, and Roderik C. Lindenbergh
EGUsphere, https://doi.org/10.5194/egusphere-2022-950, https://doi.org/10.5194/egusphere-2022-950, 2022
Short summary
Short summary
Landslides are one of the major weather related geohazards. To assess their potential impact and design mitigation solutions, a detailed understanding of the slope is required. We tested if use of machine learning, combined with satellite remote sensing data, would allow us to forecast deformation. Our results on the Vögelsberg landslide, a deep-seated landslide near Innsbruck, Austria, show that the formulation of such machine learning system is not as straightforward as often hoped for.
This article is included in the Encyclopedia of Geosciences
Edward E. Salakpi, Peter D. Hurley, James M. Muthoka, Adam B. Barrett, Andrew Bowell, Seb Oliver, and Pedram Rowhani
Nat. Hazards Earth Syst. Sci., 22, 2703–2723, https://doi.org/10.5194/nhess-22-2703-2022, https://doi.org/10.5194/nhess-22-2703-2022, 2022
Short summary
Short summary
The devastating effects of recurring drought conditions are mostly felt by pastoralists that rely on grass and shrubs as fodder for their animals. Using historical information from precipitation, soil moisture, and vegetation health data, we developed a model that can forecast vegetation condition and the probability of drought occurrence up till a 10-week lead time with an accuracy of 74 %. Our model can be adopted by policymakers and relief agencies for drought early warning and early action.
This article is included in the Encyclopedia of Geosciences
Edward E. Salakpi, Peter D. Hurley, James M. Muthoka, Andrew Bowell, Seb Oliver, and Pedram Rowhani
Nat. Hazards Earth Syst. Sci., 22, 2725–2749, https://doi.org/10.5194/nhess-22-2725-2022, https://doi.org/10.5194/nhess-22-2725-2022, 2022
Short summary
Short summary
The impact of drought may vary in a given region depending on whether it is dominated by trees, grasslands, or croplands. The differences in impact can also be the agro-ecological zones within the region. This paper proposes a hierarchical Bayesian model (HBM) for forecasting vegetation condition in spatially diverse areas. Compared to a non-hierarchical model, the HBM proved to be a more natural method for forecasting drought in areas with different land covers and
agro-ecological zones.
This article is included in the Encyclopedia of Geosciences
Weijie Zou, Yi Zhou, Shixin Wang, Futao Wang, Litao Wang, Qing Zhao, Wenliang Liu, Jinfeng Zhu, Yibing Xiong, Zhenqing Wang, and Gang Qin
Nat. Hazards Earth Syst. Sci., 22, 2081–2097, https://doi.org/10.5194/nhess-22-2081-2022, https://doi.org/10.5194/nhess-22-2081-2022, 2022
Short summary
Short summary
Landslide dams are secondary disasters caused by landslides, which can cause great damage to mountains. We have proposed a procedure to calculate the key parameters of these dams that uses only a single remote-sensing image and a pre-landslide DEM combined with landslide theory. The core of this study is a modeling problem. We have found the bridge between the theory of landslide dams and the requirements of disaster relief.
This article is included in the Encyclopedia of Geosciences
C. Scott Watson, John R. Elliott, Susanna K. Ebmeier, María Antonieta Vásquez, Camilo Zapata, Santiago Bonilla-Bedoya, Paulina Cubillo, Diego Francisco Orbe, Marco Córdova, Jonathan Menoscal, and Elisa Sevilla
Nat. Hazards Earth Syst. Sci., 22, 1699–1721, https://doi.org/10.5194/nhess-22-1699-2022, https://doi.org/10.5194/nhess-22-1699-2022, 2022
Short summary
Short summary
We assess how greenspaces could guide risk-informed planning and reduce disaster risk for the urbanising city of Quito, Ecuador, which experiences earthquake, volcano, landslide, and flood hazards. We use satellite data to evaluate the use of greenspaces as safe spaces following an earthquake. We find disparities regarding access to and availability of greenspaces. The availability of greenspaces that could contribute to community resilience is high; however, many require official designation.
This article is included in the Encyclopedia of Geosciences
Seth Bryant, Heather McGrath, and Mathieu Boudreault
Nat. Hazards Earth Syst. Sci., 22, 1437–1450, https://doi.org/10.5194/nhess-22-1437-2022, https://doi.org/10.5194/nhess-22-1437-2022, 2022
Short summary
Short summary
The advent of new satellite technologies improves our ability to study floods. While the depth of water at flooded buildings is generally the most important variable for flood researchers, extracting this accurately from satellite data is challenging. The software tool presented here accomplishes this, and tests show the tool is more accurate than competing tools. This achievement unlocks more detailed studies of past floods and improves our ability to plan for and mitigate disasters.
This article is included in the Encyclopedia of Geosciences
Tadas Nikonovas, Allan Spessa, Stefan H. Doerr, Gareth D. Clay, and Symon Mezbahuddin
Nat. Hazards Earth Syst. Sci., 22, 303–322, https://doi.org/10.5194/nhess-22-303-2022, https://doi.org/10.5194/nhess-22-303-2022, 2022
Short summary
Short summary
Extreme fire episodes in Indonesia emit large amounts of greenhouse gasses and have negative effects on human health in the region. In this study we show that such burning events can be predicted several months in advance in large parts of Indonesia using existing seasonal climate forecasts and forest cover change datasets. A reliable early fire warning system would enable local agencies to prepare and mitigate the worst of the effects.
This article is included in the Encyclopedia of Geosciences
Yahong Liu and Jin Zhang
Nat. Hazards Earth Syst. Sci., 22, 227–244, https://doi.org/10.5194/nhess-22-227-2022, https://doi.org/10.5194/nhess-22-227-2022, 2022
Short summary
Short summary
Through a comprehensive analysis of the current remote sensing technology resources, this paper establishes the database to realize the unified management of heterogeneous sensor resources and proposes a capability evaluation method of remote sensing cooperative technology in geohazard emergencies, providing a decision-making basis for the establishment of remote sensing cooperative observations in geohazard emergencies.
This article is included in the Encyclopedia of Geosciences
Diego Guenzi, Danilo Godone, Paolo Allasia, Nunzio Luciano Fazio, Michele Perrotti, and Piernicola Lollino
Nat. Hazards Earth Syst. Sci., 22, 207–212, https://doi.org/10.5194/nhess-22-207-2022, https://doi.org/10.5194/nhess-22-207-2022, 2022
Short summary
Short summary
In the Apulia region (southeastern Italy) we are monitoring a soft-rock coastal cliff using webcams and strain sensors. In this urban and touristic area, coastal recession is extremely rapid and rockfalls are very frequent. In our work we are using low-cost and open-source hardware and software, trying to correlate both meteorological information with measures obtained from crack meters and webcams, aiming to recognize potential precursor signals that could be triggered by instability phenomena.
This article is included in the Encyclopedia of Geosciences
Natalie Brožová, Tommaso Baggio, Vincenzo D'Agostino, Yves Bühler, and Peter Bebi
Nat. Hazards Earth Syst. Sci., 21, 3539–3562, https://doi.org/10.5194/nhess-21-3539-2021, https://doi.org/10.5194/nhess-21-3539-2021, 2021
Short summary
Short summary
Surface roughness plays a great role in natural hazard processes but is not always well implemented in natural hazard modelling. The results of our study show how surface roughness can be useful in representing vegetation and ground structures, which are currently underrated. By including surface roughness in natural hazard modelling, we could better illustrate the processes and thus improve hazard mapping, which is crucial for infrastructure and settlement planning in mountainous areas.
This article is included in the Encyclopedia of Geosciences
Hugues Brenot, Nicolas Theys, Lieven Clarisse, Jeroen van Gent, Daniel R. Hurtmans, Sophie Vandenbussche, Nikolaos Papagiannopoulos, Lucia Mona, Timo Virtanen, Andreas Uppstu, Mikhail Sofiev, Luca Bugliaro, Margarita Vázquez-Navarro, Pascal Hedelt, Michelle Maree Parks, Sara Barsotti, Mauro Coltelli, William Moreland, Simona Scollo, Giuseppe Salerno, Delia Arnold-Arias, Marcus Hirtl, Tuomas Peltonen, Juhani Lahtinen, Klaus Sievers, Florian Lipok, Rolf Rüfenacht, Alexander Haefele, Maxime Hervo, Saskia Wagenaar, Wim Som de Cerff, Jos de Laat, Arnoud Apituley, Piet Stammes, Quentin Laffineur, Andy Delcloo, Robertson Lennart, Carl-Herbert Rokitansky, Arturo Vargas, Markus Kerschbaum, Christian Resch, Raimund Zopp, Matthieu Plu, Vincent-Henri Peuch, Michel Van Roozendael, and Gerhard Wotawa
Nat. Hazards Earth Syst. Sci., 21, 3367–3405, https://doi.org/10.5194/nhess-21-3367-2021, https://doi.org/10.5194/nhess-21-3367-2021, 2021
Short summary
Short summary
The purpose of the EUNADICS-AV (European Natural Airborne Disaster Information and Coordination System for Aviation) prototype early warning system (EWS) is to develop the combined use of harmonised data products from satellite, ground-based and in situ instruments to produce alerts of airborne hazards (volcanic, dust, smoke and radionuclide clouds), satisfying the requirement of aviation air traffic management (ATM) stakeholders (https://cordis.europa.eu/project/id/723986).
This article is included in the Encyclopedia of Geosciences
Johnny Douvinet, Anna Serra-Llobet, Esteban Bopp, and G. Mathias Kondolf
Nat. Hazards Earth Syst. Sci., 21, 2899–2920, https://doi.org/10.5194/nhess-21-2899-2021, https://doi.org/10.5194/nhess-21-2899-2021, 2021
Short summary
Short summary
This study proposes to combine results of research regarding the spatial inequalities due to the siren coverage, the political dilemma of siren activation, and the social problem of siren awareness and trust for people in France. Surveys were conducted using a range of complementary methods (GIS analysis, statistical analysis, questionnaires, interviews) through different scales. Results show that siren coverage in France is often determined by population density but not risks or disasters.
This article is included in the Encyclopedia of Geosciences
Fabio Brighenti, Francesco Carnemolla, Danilo Messina, and Giorgio De Guidi
Nat. Hazards Earth Syst. Sci., 21, 2881–2898, https://doi.org/10.5194/nhess-21-2881-2021, https://doi.org/10.5194/nhess-21-2881-2021, 2021
Short summary
Short summary
In this paper we propose a methodology to mitigate hazard in a natural environment in an urbanized context. The deformation of the ground is a precursor of paroxysms in mud volcanoes. Therefore, through the analysis of the deformation supported by a statistical approach, this methodology was tested to reduce the hazard around the mud volcano. In the future, the goal is that this dangerous area will become both a naturalistic heritage and a source of development for the community of the area.
This article is included in the Encyclopedia of Geosciences
Doris Hermle, Markus Keuschnig, Ingo Hartmeyer, Robert Delleske, and Michael Krautblatter
Nat. Hazards Earth Syst. Sci., 21, 2753–2772, https://doi.org/10.5194/nhess-21-2753-2021, https://doi.org/10.5194/nhess-21-2753-2021, 2021
Short summary
Short summary
Multispectral remote sensing imagery enables landslide detection and monitoring, but its applicability to time-critical early warning is rarely studied. We present a concept to operationalise its use for landslide early warning, aiming to extend lead time. We tested PlanetScope and unmanned aerial system images on a complex mass movement and compared processing times to historic benchmarks. Acquired data are within the forecasting window, indicating the feasibility for landslide early warning.
This article is included in the Encyclopedia of Geosciences
Michal Bíl, Pavel Raška, Lukáš Dolák, and Jan Kubeček
Nat. Hazards Earth Syst. Sci., 21, 2581–2596, https://doi.org/10.5194/nhess-21-2581-2021, https://doi.org/10.5194/nhess-21-2581-2021, 2021
Short summary
Short summary
The online landslide database CHILDA (Czech Historical Landslide Database) summarises information about landslides which occurred in the area of Czechia (the Czech Republic). The database is freely accessible via the https://childa.cz/ website. It includes 699 records (spanning the period of 1132–1989). Overall, 55 % of all recorded landslide events occurred only within 15 years of the extreme landslide incidence.
This article is included in the Encyclopedia of Geosciences
Enrique Guillermo Cordaro, Patricio Venegas-Aravena, and David Laroze
Nat. Hazards Earth Syst. Sci., 21, 1785–1806, https://doi.org/10.5194/nhess-21-1785-2021, https://doi.org/10.5194/nhess-21-1785-2021, 2021
Short summary
Short summary
We developed a methodology that generates free externally disturbed magnetic variations in ground magnetometers close to the Chilean convergent margin. Spectral analysis (~ mHz) and magnetic anomalies increased prior to large Chilean earthquakes (Maule 2010, Mw 8.8; Iquique 2014, Mw 8.2; Illapel 2015, Mw 8.3). These findings relate to microcracks within the lithosphere due to stress state changes. This physical evidence should be thought of as a last stage of the earthquake preparation process.
This article is included in the Encyclopedia of Geosciences
Corey M. Scheip and Karl W. Wegmann
Nat. Hazards Earth Syst. Sci., 21, 1495–1511, https://doi.org/10.5194/nhess-21-1495-2021, https://doi.org/10.5194/nhess-21-1495-2021, 2021
Short summary
Short summary
For many decades, natural disasters have been monitored by trained analysts using multiple satellite images to observe landscape change. This approach is incredibly useful, but our new tool, HazMapper, offers researchers and the scientifically curious public a web-accessible
cloud-based tool to perform similar analysis. We intend for the tool to both be used in scientific research and provide rapid response to global natural disasters like landslides, wildfires, and volcanic eruptions.
This article is included in the Encyclopedia of Geosciences
Matti Wiegmann, Jens Kersten, Hansi Senaratne, Martin Potthast, Friederike Klan, and Benno Stein
Nat. Hazards Earth Syst. Sci., 21, 1431–1444, https://doi.org/10.5194/nhess-21-1431-2021, https://doi.org/10.5194/nhess-21-1431-2021, 2021
Short summary
Short summary
In this paper, we study when social media is an adequate source to find metadata about incidents that cannot be acquired by traditional means. We identify six major use cases: impact assessment and verification of model predictions, narrative generation, recruiting citizen volunteers, supporting weakly institutionalized areas, narrowing surveillance areas, and reporting triggers for periodical surveillance.
This article is included in the Encyclopedia of Geosciences
Hui Liu, Ya Hao, Wenhao Zhang, Hanyue Zhang, Fei Gao, and Jinping Tong
Nat. Hazards Earth Syst. Sci., 21, 1179–1194, https://doi.org/10.5194/nhess-21-1179-2021, https://doi.org/10.5194/nhess-21-1179-2021, 2021
Short summary
Short summary
We trained a recurrent neural network model to classify microblogging posts related to urban waterlogging and establish an online monitoring system of urban waterlogging caused by flood disasters. We manually curated more than 4400 waterlogging posts to train the RNN model so that it can precisely identify waterlogging-related posts of Sina Weibo to timely determine urban waterlogging.
This article is included in the Encyclopedia of Geosciences
Roope Tervo, Ilona Láng, Alexander Jung, and Antti Mäkelä
Nat. Hazards Earth Syst. Sci., 21, 607–627, https://doi.org/10.5194/nhess-21-607-2021, https://doi.org/10.5194/nhess-21-607-2021, 2021
Short summary
Short summary
Predicting the number of power outages caused by extratropical storms is a key challenge for power grid operators. We introduce a novel method to predict the storm severity for the power grid employing ERA5 reanalysis data combined with a forest inventory. The storms are first identified from the data and then classified using several machine-learning methods. While there is plenty of room to improve, the results are already usable, with support vector classifier providing the best performance.
This article is included in the Encyclopedia of Geosciences
Michaela Wenner, Clément Hibert, Alec van Herwijnen, Lorenz Meier, and Fabian Walter
Nat. Hazards Earth Syst. Sci., 21, 339–361, https://doi.org/10.5194/nhess-21-339-2021, https://doi.org/10.5194/nhess-21-339-2021, 2021
Short summary
Short summary
Mass movements constitute a risk to property and human life. In this study we use machine learning to automatically detect and classify slope failure events using ground vibrations. We explore the influence of non-ideal though commonly encountered conditions: poor network coverage, small number of events, and low signal-to-noise ratios. Our approach enables us to detect the occurrence of rare events of high interest in a large data set of more than a million windowed seismic signals.
This article is included in the Encyclopedia of Geosciences
Luiz Felipe Galizia, Thomas Curt, Renaud Barbero, and Marcos Rodrigues
Nat. Hazards Earth Syst. Sci., 21, 73–86, https://doi.org/10.5194/nhess-21-73-2021, https://doi.org/10.5194/nhess-21-73-2021, 2021
Short summary
Short summary
This paper aims to provide a quantitative evaluation of three remotely sensed fire datasets which have recently emerged as an important resource to improve our understanding of fire regimes. Our findings suggest that remotely sensed fire datasets can be used to proxy variations in fire activity on monthly and annual timescales; however, caution is advised when drawing information from smaller fires (< 100 ha) across the Mediterranean region.
This article is included in the Encyclopedia of Geosciences
Philippe Weyrich, Anna Scolobig, Florian Walther, and Anthony Patt
Nat. Hazards Earth Syst. Sci., 20, 2811–2821, https://doi.org/10.5194/nhess-20-2811-2020, https://doi.org/10.5194/nhess-20-2811-2020, 2020
Patric Kellermann, Kai Schröter, Annegret H. Thieken, Sören-Nils Haubrock, and Heidi Kreibich
Nat. Hazards Earth Syst. Sci., 20, 2503–2519, https://doi.org/10.5194/nhess-20-2503-2020, https://doi.org/10.5194/nhess-20-2503-2020, 2020
Short summary
Short summary
The flood damage database HOWAS 21 contains object-specific flood damage data resulting from fluvial, pluvial and groundwater flooding. The datasets incorporate various variables of flood hazard, exposure, vulnerability and direct tangible damage at properties from several economic sectors. This paper presents HOWAS 21 and highlights exemplary analyses to demonstrate the use of HOWAS 21 flood damage data.
This article is included in the Encyclopedia of Geosciences
Giuseppe Esposito, Ivan Marchesini, Alessandro Cesare Mondini, Paola Reichenbach, Mauro Rossi, and Simone Sterlacchini
Nat. Hazards Earth Syst. Sci., 20, 2379–2395, https://doi.org/10.5194/nhess-20-2379-2020, https://doi.org/10.5194/nhess-20-2379-2020, 2020
Short summary
Short summary
In this article, we present an automatic processing chain aimed to support the detection of landslides that induce sharp land cover changes. The chain exploits free software and spaceborne SAR data, allowing the systematic monitoring of wide mountainous regions exposed to mass movements. In the test site, we verified a general accordance between the spatial distribution of seismically induced landslides and the detected land cover changes, demonstrating its potential use in emergency management.
This article is included in the Encyclopedia of Geosciences
Mohammad Malakootian and Majid Nozari
Nat. Hazards Earth Syst. Sci., 20, 2351–2363, https://doi.org/10.5194/nhess-20-2351-2020, https://doi.org/10.5194/nhess-20-2351-2020, 2020
Short summary
Short summary
The present study estimated the Kerman–Baghin aquifer vulnerability using DRASTIC and composite DRASTIC (CDRASTIC) indices with the aid of geographic information system (GIS) techniques. The aquifer vulnerability maps indicated very similar results, identifying the north-west parts of the aquifer as areas with high to very high vulnerability. According to the results, parts of the studied aquifer have a high vulnerability and require protective measures.
This article is included in the Encyclopedia of Geosciences
Diana Contreras, Alondra Chamorro, and Sean Wilkinson
Nat. Hazards Earth Syst. Sci., 20, 1663–1687, https://doi.org/10.5194/nhess-20-1663-2020, https://doi.org/10.5194/nhess-20-1663-2020, 2020
Short summary
Short summary
The socio-economic condition of the population determines their vulnerability to earthquakes, tsunamis, volcanic eruptions, landslides, soil erosion and land degradation. This condition is estimated mainly from population censuses. The lack to access to basic services, proximity to hazard zones, poverty and population density highly influence the vulnerability of communities. Mapping the location of this vulnerable population makes it possible to prevent and mitigate their risk.
This article is included in the Encyclopedia of Geosciences
Simona Colombelli, Francesco Carotenuto, Luca Elia, and Aldo Zollo
Nat. Hazards Earth Syst. Sci., 20, 921–931, https://doi.org/10.5194/nhess-20-921-2020, https://doi.org/10.5194/nhess-20-921-2020, 2020
Short summary
Short summary
We developed a mobile app for Android devices which receives the alerts generated by a network-based early warning system, predicts the expected ground-shaking intensity and the available lead time at the user position, and provides customized messages to inform the user about the proper reaction to the alert. The app represents a powerful tool for informing in real time a wide audience of end users and stakeholders about the potential damaging shaking in the occurrence of an earthquake.
This article is included in the Encyclopedia of Geosciences
Richard Styron, Julio García-Pelaez, and Marco Pagani
Nat. Hazards Earth Syst. Sci., 20, 831–857, https://doi.org/10.5194/nhess-20-831-2020, https://doi.org/10.5194/nhess-20-831-2020, 2020
Short summary
Short summary
The Caribbean and Central American region is both tectonically active and densely populated, leading to a large population that is exposed to earthquake hazards. Until now, no comprehensive fault data covering the region have been available. We present a new public fault database for Central America and the Caribbean that synthesizes published studies with new mapping from remote sensing to provide fault sources for the CCARA seismic hazard and risk analysis project and to aid future research.
This article is included in the Encyclopedia of Geosciences
María del Pilar Jiménez-Donaire, Ana Tarquis, and Juan Vicente Giráldez
Nat. Hazards Earth Syst. Sci., 20, 21–33, https://doi.org/10.5194/nhess-20-21-2020, https://doi.org/10.5194/nhess-20-21-2020, 2020
Short summary
Short summary
A new combined drought indicator (CDI) is proposed that integrates rainfall, soil moisture and vegetation dynamics. The performance of this indicator was evaluated against crop damage data from agricultural insurance schemes in five different areas in SW Spain. Results show that this indicator was able to predict important droughts in 2004–2005 and 2011–2012, marked by crop damage of between 70 % and 95 % of the total insured area. This opens important applications for improving insurance schemes.
This article is included in the Encyclopedia of Geosciences
Quancai Xie, Qiang Ma, Jingfa Zhang, and Haiying Yu
Nat. Hazards Earth Syst. Sci., 19, 2827–2839, https://doi.org/10.5194/nhess-19-2827-2019, https://doi.org/10.5194/nhess-19-2827-2019, 2019
Short summary
Short summary
This paper evaluates a new method for modeling the site amplification factor. Through implementing this method and making simulations for different cases, we find that this method shows better performance than the previous method and JMA report. We better understand the advantages and disadvantages of this method, although there are some problems that need to be considered carefully and solved; it shows good potential to be used in future earthquake early warning systems.
This article is included in the Encyclopedia of Geosciences
David A. Bonneau, D. Jean Hutchinson, Paul-Mark DiFrancesco, Melanie Coombs, and Zac Sala
Nat. Hazards Earth Syst. Sci., 19, 2745–2765, https://doi.org/10.5194/nhess-19-2745-2019, https://doi.org/10.5194/nhess-19-2745-2019, 2019
Short summary
Short summary
In mountainous regions around the world rockfalls pose a hazard to infrastructure and society. To aid in our understanding and management of these complex hazards, an inventory can be compiled. Three-dimensional remote sensing data can be used to locate the source zones of these events and generate models of areas which detached. We address the way in which the shape of a rockfall object can be measured. The shape of a rockfall has implications for forward modelling of potential runout zones.
This article is included in the Encyclopedia of Geosciences
Kwonmin Lee, Hye-Sil Kim, and Yong-Sang Choi
Nat. Hazards Earth Syst. Sci., 19, 2241–2248, https://doi.org/10.5194/nhess-19-2241-2019, https://doi.org/10.5194/nhess-19-2241-2019, 2019
Short summary
Short summary
This study examined the advances in the predictability of thunderstorms using geostationary satellite imageries. Our present results show that by using the latest geostationary satellite data (with a resolution of 2 km and 10 min), thunderstorms can be predicted 90–180 min ahead of their mature state. These data can capture the rapidly growing cloud tops before the cloud moisture falls as precipitation and enable prompt preparation and the mitigation of hazards.
This article is included in the Encyclopedia of Geosciences
Qingyun Zhang, Yongsheng Li, Jingfa Zhang, and Yi Luo
Nat. Hazards Earth Syst. Sci., 19, 2229–2240, https://doi.org/10.5194/nhess-19-2229-2019, https://doi.org/10.5194/nhess-19-2229-2019, 2019
Short summary
Short summary
Before the opening of the railway, the deformation of the Qinghai–Tibet Railway was very small and considered stable. After opening, the overall stability of the railway section was good. The main deformation areas are concentrated in the areas where railway lines turn and geological disasters are concentrated. In order to ensure the safety of railway operation, it is necessary to carry out long-term time series observation along the Qinghai–Tibet Railway.
This article is included in the Encyclopedia of Geosciences
Xiao Huang, Cuizhen Wang, and Junyu Lu
Nat. Hazards Earth Syst. Sci., 19, 2141–2155, https://doi.org/10.5194/nhess-19-2141-2019, https://doi.org/10.5194/nhess-19-2141-2019, 2019
Short summary
Short summary
This study examined the spatiotemporal dynamics of nighttime satellite-derived human settlement in response to different levels of hurricane proneness in a period from 1992 to 2013. It confirms the
This article is included in the Encyclopedia of Geosciences
Snow Belt-to-Sun BeltUS population shift trend. The results also suggest that hurricane-exposed human settlement has grown in extent and area, as more hurricane exposure has experienced a larger increase rate in settlement intensity.
Maja Kucharczyk and Chris H. Hugenholtz
Nat. Hazards Earth Syst. Sci., 19, 2039–2051, https://doi.org/10.5194/nhess-19-2039-2019, https://doi.org/10.5194/nhess-19-2039-2019, 2019
Short summary
Short summary
We performed pre-disaster 3-D mapping with a drone in downtown Victoria, BC, Canada. This was the first drone mapping mission over a Canadian city approved by Canada’s aviation authority. We were legally constrained to using a specific drone. The goal was to assess the quality of the 3-D map. Results indicate that the spatial accuracies achieved with this drone would allow for sub-meter building collapse detection, but the non-tilting camera was insufficient for mapping buildings in 3-D.
This article is included in the Encyclopedia of Geosciences
Reza Hassanzadeh, Mehdi Honarmand, Mahdieh Hossienjani Zadeh, and Farzin Naseri
Nat. Hazards Earth Syst. Sci., 19, 1989–2009, https://doi.org/10.5194/nhess-19-1989-2019, https://doi.org/10.5194/nhess-19-1989-2019, 2019
Short summary
Short summary
This paper proposes a new model for evaluating local seismic amplification susceptibility by considering direct characteristics of influencing criteria and dealing with uncertainty of modelling through production of fuzzy membership functions and GIS. This model helps planners and decision makers easily produce local seismic amplification susceptibility to be incorporated in designing development plans of urban areas and to evaluate safety measures of existing infrastructure.
This article is included in the Encyclopedia of Geosciences
Salvador Gil-Guirado, Alfredo Pérez-Morales, and Francisco Lopez-Martinez
Nat. Hazards Earth Syst. Sci., 19, 1955–1971, https://doi.org/10.5194/nhess-19-1955-2019, https://doi.org/10.5194/nhess-19-1955-2019, 2019
Short summary
Short summary
In this study the SMC-Flood database for the municipalities of the Mediterranean coast of mainland Spain is presented. This database has enabled the reconstruction of 3008 cases of flooding on a municipal scale between 1960 and 2015. The data analysis reveals a growing trend in the frequency and area affected by flood cases. The main novelty lies in the fact that we have detected a clear latitudinal gradient of growing intensity and severity of flood cases with a north–south direction.
This article is included in the Encyclopedia of Geosciences
Juan José Martín-Sotoca, Antonio Saa-Requejo, Rubén Moratiel, Nicolas Dalezios, Ioannis Faraslis, and Ana María Tarquis
Nat. Hazards Earth Syst. Sci., 19, 1685–1702, https://doi.org/10.5194/nhess-19-1685-2019, https://doi.org/10.5194/nhess-19-1685-2019, 2019
Short summary
Short summary
Vegetation indices based on satellite images, such as the normalized difference vegetation index (NDVI), have been used for damaged pasture insurance. The occurrence of damage is usually defined by NDVI thresholds mainly based on normal statistics. In this work a pasture area in Spain was delimited by MODIS images. A statistical analysis of NDVI was applied to search for alternative distributions. Results show that generalized extreme value distributions present a better fit than normal ones.
This article is included in the Encyclopedia of Geosciences
Hongyan Chen, Gengxing Zhao, Yuhuan Li, Danyang Wang, and Ying Ma
Nat. Hazards Earth Syst. Sci., 19, 1499–1508, https://doi.org/10.5194/nhess-19-1499-2019, https://doi.org/10.5194/nhess-19-1499-2019, 2019
Short summary
Short summary
Using Landsat data, the inversion model of soil salt content (SSC) for different seasons was determined in the Kenli District in the Yellow River Delta region of China. The SSC exhibited a gradual increasing trend from the southwest to northeast. The SSC accumulated in spring, decreased in summer, increased in autumn and reached its peak at the the end of winter. The results can provide data for the control of soil salt hazards and utilization of saline–alkali soil.
This article is included in the Encyclopedia of Geosciences
Jérome Faillettaz, Martin Funk, Jan Beutel, and Andreas Vieli
Nat. Hazards Earth Syst. Sci., 19, 1399–1413, https://doi.org/10.5194/nhess-19-1399-2019, https://doi.org/10.5194/nhess-19-1399-2019, 2019
Short summary
Short summary
We developed a new strategy for real-time early warning of
gravity-driven slope failures (such as landslides, rockfalls, glacier break-off, etc.). This method enables us to investigate natural slope stability based on continuous monitoring and interpretation of seismic waves generated by the potential instability. Thanks to a pilot experiment, we detected typical patterns of precursory events prior to slide events, demonstrating the potential of this method for real-word applications.
This article is included in the Encyclopedia of Geosciences
Cited articles
Alam, F., Imran, M., and Ofli, F.: Image4Act: Online Social Media Image
Processing for Disaster Response, in: Proceedings of the 2017 IEEE/ACM
International Conference on Advances in Social Networks Analysis and Mining
2017, ASONAM ’17, 601–604, 2017. a
Alam, F., Ofli, F., and Imran, M.: Descriptive and visual summaries of disaster
events using artificial intelligence techniques: case studies of Hurricanes
Harvey, Irma, and Maria, Behav. Inform. Technol., 39, 288–318,
https://doi.org/10.1080/0144929X.2019.1610908, 2020. a
ALRashdi, R. and O'Keefe, S.: Deep Learning and Word Embeddings for Tweet
Classification for Crisis Response, The 3rd National Computing Colleges Conference, 8–9 October 2018, Abha, Saudi Arabia, 2019. a
American Red Cross: Social Media in Disasters and Emergencies, Tech. rep., American Red Cross, online study, 2010. a
Barnwal, D., Ghelani, S., Krishna, R., Basu, M., and Ghosh, S.: Identifying
Fact-Checkable Microblogs during Disasters: A Classification-Ranking
Approach, ICDCN '19, 389–392, Association for Computing Machinery, New
York, NY, USA, https://doi.org/10.1145/3288599.3295587, 2019. a
Birant, D. and Kut, A.: ST-DBSCAN: An algorithm for clustering spatial-temporal
data, Data Knowl. Eng., 60, 208–221,
2007. a
Blei, D. M., Ng, A. Y., and Jordan, M. I.: Latent dirichlet allocation, J.
Mach. Learn. Res., 3, 993–1022, 2003. a
Brouwer, T., Eilander, D., van Loenen, A., Booij, M. J., Wijnberg, K. M., Verkade, J. S., and Wagemaker, J.: Probabilistic flood extent estimates from social media flood observations, Nat. Hazards Earth Syst. Sci., 17, 735–747, https://doi.org/10.5194/nhess-17-735-2017, 2017. a
Burel, G. and Alani, H.: Crisis Event Extraction Service (CREES) –
Automatic Detection and Classification of Crisis-related Content on
Social Media, in: Proceedings of the 15th International Conference on
Information Systems for Crisis Response and Management (ISCRAM), p. 12, 20–23 May 2018, Rochester, NY, USA, 2018. a, b, c, d, e, f
Burel, G., Saif, H., Fernandez, M., and Alani, H.: On semantics and deep
learning for event detection in crisis situations, in: Workshop on Semantic
Deep Learning (SemDeep), at ESWC 2017, 19 September 2017, Montpellier, France, 2017b. a
Casalino, G., Castiello, C., Buono, N. D., and Mencar, C.: A framework for
intelligent Twitter data analysis with non-negative matrix factorization,
IJWIS, 14, 334–356, 2018. a
Cer, D., Yang, Y., Kong, S., Hua, N., Limtiaco, N. L. U., John, R. S.,
Constant, N., Guajardo-Céspedes, M., Yuan, S., Tar, C., Sung, Y., Strope,
B., and Kurzweil, R.: Universal Sentence Encoder, arXiv:1803.11175, 2018. a
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., and Kuksa,
P.: Natural Language Processing (Almost) from Scratch, J. Mach. Learn. Res.,
999888, 2493–2537, 2011. a
Crowdflower: https://data.world/crowdflower/disasters-on-social-media (last access: 1 June 2021),
2015. a
de Albuquerque, J. P., Herfort, B., Brenning, A., and Zipf, A.: A geographic
approach for combining social media and authoritative data towards
identifying useful information for disaster management, International Journal
of Geographical Information Science, 29, 667–689, 2015. a
de Bruijn, J. A., de Moel, H., Weerts, A. H., de Ruiter, M. C., Basar, E.,
Eilander, D., and Aerts, J. C. J. H.: Improving the classification of flood
tweets with contextual hydrological information in a multimodal neural
network, Comput. Geosci., 140, 104485, https://doi.org/10.1016/j.cageo.2020.104485, 2020. a, b, c, d
de Miranda, G. R., Pasti, R., and de Castro, L. N.: Detecting Topics in
Documents by Clustering Word Vectors, in: Distributed Computing and
Artificial Intelligence, 16th International Conference, edited by: Herrera,
F., Matsui, K., and Rodríguez-González, S., 235–243, Springer
International Publishing, Cham, 2020. a
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K.: BERT: Pre-training of
Deep Bidirectional Transformers for Language Understanding, in: Proceedings
of the 2019 Conference of the North American Chapter of the Association for
Computational Linguistics: Human Language Technologies, Volume 1 (Long and
Short Papers), Association for Computational Linguistics, Minneapolis,
Minnesota, 2019. a
Ertugrul, A. M., Velioglu, B., and Karagoz, P.: Word Embedding Based Event
Detection on Social Media, in: Hybrid Artificial Intelligent Systems, edited
by: Martínez de Pisón, F. J., Urraca, R., Quintián, H., and
Corchado, E., 3–14, Springer International Publishing, Cham, 2017. a
Ester, M., Kriegel, H.-P., Sander, J., and Xu, X.: A Density-based Algorithm
for Discovering Clusters a Density-based Algorithm for Discovering Clusters
in Large Spatial Databases with Noise, in: Proceedings of the Second
International Conference on Knowledge Discovery and Data Mining, KDD'96,
226–231, AAAI Press, 2–4 August 1996, Portland, OR, USA, 1996. a
Fathi, R., Thom, D., Koch, S., Ertl, T., and Fiedrich, F.: VOST: A case study
in voluntary digital participation for collaborative emergency management,
Inform. Process. Manag., 57, 102174, https://doi.org/10.1016/j.ipm.2019.102174, 2020. a
Fedoryszak, M., Frederick, B., Rajaram, V., and Zhong, C.: Real-Time Event
Detection on Social Data Streams, in: Proceedings of the 25th ACM SIGKDD
International Conference on Knowledge Discovery & Data Mining, KDD '19,
2774–2782, Association for Computing Machinery, New York, NY, USA,
https://doi.org/10.1145/3292500.3330689, 2019. a
Habdank, M., Rodehutskors, N., and Koch, R.: Relevancy assessment of tweets
using supervised learning techniques: Mining emergency related tweets for
automated relevancy classification, 4th International Conference on Information and Communication Technologies for Disaster Management (ICT-DM), 11–13 December 2017, Muenster, Germany, 2017. a, b
Hadifar, A., Sterckx, L., Demeester, T., and Develder, C.: A Self-Training
Approach for Short Text Clustering, in: Proceedings of the 4th Workshop on
Representation Learning for NLP (RepL4NLP-2019), 194–199, Association
for Computational Linguistics, Florence, Italy, https://doi.org/10.18653/v1/W19-4322,
2019. a
Havas, C., Resch, B., Francalanci, C., Pernici, B., Scalia, G.,
Fernandez-Marquez, J. L., Van Achte, T., Zeug, G., Mondardini, M. R. R.,
Grandoni, D., Kirsch, B., Kalas, M., Lorini, V., and Rüping, S.: E2mC:
Improving Emergency Management Service Practice through Social Media and
Crowdsourcing Analysis in Near Real Time, Sensors, 17, https://doi.org/10.3390/s17122766, 2017. a
He, X., Lu, D., Margolin, D., Wang, M., Idrissi, S. E., and Lin, Y.-R.: The
Signals and Noise: Actionable Information in Improvised Social Media Channels
During a Disaster, in: Proceedings of the 2017 ACM on Web Science Conference,
WebSci '17, 33–42, Association for Computing Machinery, New York, NY,
USA, 2017. a
Ibtihel, B. L., Lobna, H., and Lotfi, B. R.: A Deep Learning-based Ranking
Approach for Microblog Retrieval, Procedia Comput. Sci., 159, 352–362,
https://doi.org/10.1016/j.procs.2019.09.190, 2019. a
Imran, M., Castillo, C., Lucas, J., Meier, P., and Vieweg, S.: AIDR: Artificial
Intelligence for Disaster Response, in: Proceedings of the 23rd International
Conference on World Wide Web, WWW ’14 Companion, 159–162, Association
for Computing Machinery, New York, NY, USA, 2014. a
Imran, M., Castillo, C., Diaz, F., and Vieweg, S.: Processing Social Media
Messages in Mass Emergency: A Survey, ACM Comput. Surv., 47,
1–38, https://doi.org/10.1145/2771588, 2015. a, b
Imran, M., Mitra, P., and Castillo, C.: Twitter as a Lifeline: Human-annotated
Twitter Corpora for NLP of Crisis-related Messages, in: Proceedings of the
10th International Conference on Language Resources and Evaluation (LREC),
European Language Resources Association (ELRA), Paris, France,
2016a. a, b
Imran, M., Mitra, P., and Srivastava, J.: Enabling Rapid Classification of
Social Media Communications During Crises, Int. J. Inf. Syst. Crisis Response
Manag., 8, 1–17, 2016c. a
Imran, M., Castillo, C., Diaz, F., and Vieweg, S.: Processing Social Media
Messages in Mass Emergency: Survey Summary, in: Companion Proceedings of the
The Web Conference 2018, WWW '18, 507–511, International World Wide Web
Conferences Steering Committee, Republic and Canton of Geneva, Switzerland,
2018. a
Joulin, A., Grave, E., Bojanowski, P., and Mikolov, T.: Bag of Tricks for
Efficient Text Classification, EACL 2017, 3–7 April 2017, Valencia, Spain, 2016. a
Kejriwal, M. and Zhou, P.: Low-supervision Urgency Detection and Transfer in
Short Crisis Messages, arXiv:1907.06745, 2019. a, b, c
Kersten, J., Bongard, J. H., and Klan, F.: Combining Supervised and
Unsupervised Learning to Detect and Semantically Aggregate Crisis-Related
Twitter Content, in: International Conference on Information Systems for
Crisis Response and Management (ISCRAM), 2021. a
Kropczynski, J., Grace, R., Coche, J., Halse, S., Obeysekare, E., Montarnal,
A., Benaben, F., and Tapia, A.: Identifying Actionable Information on Social
Media for Emergency Dispatch, in: ISCRAM Asia Pacific 2018: Innovating for
Resilience – 1st International Conference on Information Systems for Crisis
Response and Management Asia Pacific., 428–438, Wellington, New
Zealand, 2018. a
Kruspe, A.: Few-shot tweet detection in emerging disaster events, in: AI+HADR
Workshop @ NeurIPS, arXiv:1910.02290
13 December 2019, Vancouver, Canada, 2019. a
Kruspe, A., Häberle, M., Kuhn, I., and Zhu, X. X.: Cross-language sentiment
analysis of European Twitter messages during the COVID-19 pandemic, in:
Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020,
Association for Computational Linguistics, 9–10 July 2020. a
Kumar, S., Barbier, G., Abbasi, M. A., and Liu, H.: TweetTracker: An Analysis
Tool for Humanitarian and Disaster Relief, in: International AAAI Conference
on Weblogs and Social Media (ICWSM), Barcelona, Spain, 2011. a
Lample, G., Conneau, A., Ranzato, M., Denoyer, L., and Jégou, H.: Word
translation without parallel data, in: 6th International Conference on
Learning Representations, ICLR 2018, Vancouver, BC, Canada, 30 April–3 May 2018, Conference Track Proceedings, 2018. a
Landwehr, P. M. and Carley, K. M.: Social Media in Disaster Relief –
Usage Patterns, Data Mining Tools, and Current Research Directions, in: Data
Mining and Knowledge Discovery for Big Data, edited by: Chu, W. W.,
225–257, Springer Berlin Heidelberg, Berlin, Heidelberg,
https://doi.org/10.1007/978-3-642-40837-3_7, 2014. a
Lang, S., Füreder, P., Riedler, B., Wendt, L., Braun, A., Tiede, D.,
Schoepfer, E., Zeil, P., Spröhnle, K., Kulessa, K., Rogenhofer, E., Bäuerl,
M., Öze, A., Schwendemann, G., and Hochschild, V.: Earth observation tools
and services to increase the effectiveness of humanitarian assistance,
Eur. J. Remote Sens., 53, 67–85, 2020. a
Lee, J., Gong, J., and Li, S.: Exploring spatiotemporal clusters based on
extended kernel estimation methods, Int. J. Geogr. Inf. Sci., 31, 1154–1177, 2017. a
Lin, Z., Jin, H., Robinson, B., and Lin, X.: Towards an accurate social media
disaster event detection system based on deep learning and semantic
representation, in: Proceedings of the 14th Australasian Data Mining
Conference, Canberra, Australia, 6–8, 2016. a
Liu, J., Singhal, T., Blessing, L. T. M., Wood, K. L., and Lim, K. H.:
CrisisBERT: a Robust Transformer for Crisis Classification and Contextual
Crisis Embedding, 2005.06627, 2020. a
Lorini, V., Castillo, C., Dottori, F., Kalas, M., Nappo, D., and Salamon, P.:
Integrating Social Media into a Pan-European Flood Awareness System: A
Multilingual Approach, in: 16th International Conference on Information
Systems for Crisis Response and Management (ISCRAM), València, Spain,
2019. a, b, c
McCreadie, R., Macdonald, C., and Ounis, I.: EAIMS: Emergency Analysis
Identification and Management System, in: SIGIR, 1101–1104, ACM, SIGIR 2016, 17–21 July 2016, Pisa, Italy, 2016. a
Mendonça, I., Trouvé, A., Fukuda, A., and Murakami, K.:: On Clustering
Algorithms: Applications in Word-Embedding Documents, JCP, 14, 88–92, 2019. a
Mikolov, T., Chen, K., Corrado, G., and Dean, J.: Efficient Estimation of Word
Representations in Vector Space, CoRR, abs/1301.3781,
arXiv:1301.3781, 2013. a, b
Nalluru, G., Pandey, R., and Purohit, H.: Relevancy Classification of
Multimodal Social Media Streams for Emergency Services, IEEE International
Conference on Smart Computing, 12–15 June 2019, Washington, DC, USA,
available at: https://par.nsf.gov/biblio/10098177 (last access: 1 June 2021), 2019. a
Nguyen, D. T., Joty, S. R., Imran, M., Sajjad, H., and Mitra, P.: Applications
of Online Deep Learning for Crisis Response Using Social Media Information,
in: International Workshop on Social Web for Disaster Management (SWDM), Los
Angeles, CA, USA, 2016a. a
Nguyen, D. T., Al-Mannai, K. A., Joty, S., Sajjad, H., Imran, M., and Mitra,
P.: Robust classification of crisis-related data on social networks using
convolutional neural networks, in: Proceedings of the 11th International
Conference on Web and Social Media (ICWSM), 632–635, AAAI press, 15–18 May 2017 Montreal, Canada, , 2017a. a, b, c
Nguyen, D. T., Ofli, F., Imran, M., and Mitra, P.: Damage Assessment from
Social Media Imagery Data During Disasters, in: Proceedings of the 2017
IEEE/ACM International Conference on Advances in Social Networks Analysis and
Mining 2017, ASONAM ’17, 569–576, Association for Computing Machinery,
New York, NY, USA, 2017b. a
Nguyen, M. D. and Shin, W.-Y.: DBSTexC: Density-Based Spatio-Textual Clustering
on Twitter, in: Proceedings of the 2017 IEEE/ACM International Conference on
Advances in Social Networks Analysis and Mining 2017, ASONAM '17, 23–26,
Association for Computing Machinery, New York, NY, USA,
https://doi.org/10.1145/3110025.3110096, 2017. a
Niles, M. T., Emery, B. F., Reagan, A. J., Dodds, P. S., and Danforth, C. M.:
Social media usage patterns during natural hazards, PLOS ONE, 14, 1–16,
2019. a
Ning, X., Yao, L., Benatallah, B., Zhang, Y., Sheng, Q. Z., and Kanhere, S. S.:
Source-Aware Crisis-Relevant Tweet Identification and Key Information
Summarization, ACM Trans. Internet Technol., 19, https://doi.org/10.1145/3300229, 2019. a, b, c
Palen, L., Anderson, J., Bica, M., Castillos, C., Crowley, J., Díaz, P.,
Finn, M., Grace, R., Hughes, A., Imran, M., Kogan, M., LaLone, N., Mitra, P.,
Norris, W., Pine, K., Purohit, H., Reuter, C., Rizza, C., St Denis, L.,
Semaan, B., Shalin, V., Shanley, L., Shih, P., Soden, R., Starbird, K.,
Stephen, K., Toups, Z. O., and Wilson, T.: Crisis Informatics:
Human-Centered Research on Tech & Crises,
available at: https://hal.archives-ouvertes.fr/hal-02781763 (last access: 1 June 2021), working paper
or preprint, 2020. a
Pennington, J., Socher, R., and Manning, C.: GloVe: Global Vectors for Word
Representation, in: Proceedings of the 2014 Conference on Empirical Methods
in Natural Language Processing (EMNLP), 1532–1543, Association for
Computational Linguistics, Doha, Qatar, 2014. a
Poblet, M., García-Cuesta, E., and Casanovas, P.: Crowdsourcing Tools for
Disaster Management: A Review of Platforms and Methods, in: AI Approaches to
the Complexity of Legal Systems, edited by: Casanovas, P., Pagallo, U.,
Palmirani, M., and Sartor, G., 261–274, Springer Berlin Heidelberg,
Berlin, Heidelberg, 2014. a
Purohit, H., Castillo, C., Imran, M., and Pandey, R.: Social-EOC:
Serviceability Model to Rank Social Media Requests for Emergency Operation
Centers, in: IEEE/ACM 2018 International Conference on Advances in Social
Networks Analysis and Mining, ASONAM 2018, Barcelona, Spain, 28–31 August 2018, edited by: Brandes, U., Reddy, C., and Tagarelli, A., 119–126,
IEEE Computer Society, 2018. a
Qazi, U., Imran, M., and Ofli, F.: GeoCoV19: A Dataset of Hundreds of Millions
of Multilingual COVID-19 Tweets with Location Information, ACM SIGSPATIAL
Special, 12, 6–15, 2020. a
Qiang, J., Li, Y., Yuan, Y., Liu, W., and Wu, X.: A practical algorithm for
solving the sparseness problem of short text clustering, Intell. Data Anal.,
23, 701–716, 2019. a
Ramachandran, D. and Ramasubramanian, P.: Event detection from Twitter – a
survey, International Journal of Web Information Systems, 14, 262–280,
https://doi.org/10.1108/IJWIS-11-2017-0075, 2018. a
Reuter, C. and Kaufhold, M.-A.: Fifteen Years of Social Media in Emergencies: A
Retrospective Review and Future Directions for Crisis Informatics,
J. Conting. Crisis An., 26, 41–57, 2018. a
Rogstadius, J., Vukovic, M., Teixeira, C. A., Kostakos, V., Karapanos, E., and
Laredo, J. A.: CrisisTracker: Crowdsourced social media curation for
disaster awareness, IBM J. Res. Dev., 57, 4:1–4:13,
2013. a
Rossi, C.: I-REACT: Improving Resilience to Emergencies through Advanced Cyber
Technologies, Tech. rep., Istituto Superiore Mario Boella (ISMB), Turin, Italy, 2019. a
Sakaki, T., Okazaki, M., and Matsuo, Y.: Earthquake shakes Twitter users:
Real-time event detection by social sensors, in: Proceedings of the 19th
International Conference on World Wide Web, 851–860, ACM, 26–30 April 2010, Raleigh, NC, USA, 2010. a
Singh, A. K. and Shashi, M.: Vectorization of Text Documents for Identifying
Unifiable News Articles, International Journal of Advanced Computer Science
and Applications, 10, 305–310, https://doi.org/10.14569/IJACSA.2019.0100742, 2019. a, b
Sloan, L., Morgan, J., Housley, W., Williams, M., Edwards, A., Burnap, P., and
Rana, O.: Knowing the Tweeters: Deriving Sociologically Relevant Demographics
from Twitter, Sociol. Res. Online, 18, 7, https://doi.org/10.5153/sro.3001, 2013. a, b, c
Stieglitz, S., Mirbabaie, M., Fromm, J., and Melzer, S.: The Adoption of
Social Media Analytics for Crisis Management – Challenges and
Opportunities, in: Twenty-Sixth Eur. Conf. Inf. Syst. (ECIS2018), 23–28 June 2018, Portsmouth, UK, 2018. a
Stowe, K., Palmer, M., Anderson, J., Kogan, M., Palen, L., Anderson, K. M.,
Morss, R., Demuth, J., and Lazrus, H.: Developing and Evaluating Annotation
Procedures for Twitter Data during Hazard Events, in: Proceedings of the
Joint Workshop on Linguistic Annotation, Multiword Expressions and
Constructions (LAW-MWE-CxG-2018), 133–143, Association for Computational
Linguistics, Workshop at COLING 2018, 25–26 August 2018, Santa Fe, USA, 2018. a, b, c, d, e
Thomas, C., McCreadie, R., and Ounis, I.: Event Tracker: A Text Analytics
Platform for Use During Disasters, in: SIGIR, 1341–1344, ACM, SIGIR Conference, 21–25 July 2019, Paris, France, 2019. a
Twitter, Inc.: Developer Agreement and Policy,
available at: https://developer.twitter.com/en/developer-terms/agreement-and-policy (last access: 1 June 2021),
2020. a
Valkanas, G., Katakis, I., Gunopulos, D., and Stefanidis, A.: Mining Twitter
Data with Resource Constraints, in: 2014 IEEE/WIC/ACM International Joint
Conferences on Web Intelligence (WI) and Intelligent Agent Technologies
(IAT), Warsaw, Poland, 11–14 August 2014, Volume II, 157–164, IEEE
Computer Society, 2014. a
Viegas, F., Canuto, S., Gomes, C., Luiz, W., Rosa, T., Ribas, S., Rocha, L.,
and Gonçalves, M. A.: CluWords: Exploiting Semantic Word Clustering
Representation for Enhanced Topic Modeling, in: Proceedings of the Twelfth
ACM International Conference on Web Search and Data Mining, WSDM '19, 753–761, Association for Computing Machinery, New York, NY, USA,
https://doi.org/10.1145/3289600.3291032, 2019. a
Voigt, S., Giulio-Tonolo, F., Lyons, J., Kučera, J., Jones, B.,
Schneiderhan, T., Platzeck, G., Kaku, K., Hazarika, M. K., Czaran, L., Li,
S., Pedersen, W., James, G. K., Proy, C., Muthike, D. M., Bequignon, J., and
Guha-Sapir, D.: Global trends in satellite-based emergency mapping, Science,
353, 247–252, 2016. a
Wiegmann, M., Kersten, J., Klan, F., Potthast, M., and Stein, B.: Disaster
Tweet Corpus 2020, Zenodo, https://doi.org/10.5281/zenodo.3713920, 2020. a, b, c
Wiegmann, M., Kersten, J., Senaratne, H., Potthast, M., Klan, F., and Stein, B.: Opportunities and risks of disaster data from social media: a systematic review of incident information, Nat. Hazards Earth Syst. Sci., 21, 1431–1444, https://doi.org/10.5194/nhess-21-1431-2021, 2021b. a
Xu, Z., Liu, Y., Yen, N. Y., Mei, L., Luo, X., Wei, X., and Hu,
C.: Crowdsourcing Based Description of Urban Emergency Events Using Social
Media Big Data, IEEE Transactions on Cloud Computing, 8, 387–397, 2020. a
Yang, Y., Cer, D., Ahmad, A., Guo, M., Law, J., Constant, N., Abrego, G. H.,
Yuan, S., Tar, C., Sung, Y.-H., Strope, B., and Kurzweil, R.: Multilingual
Universal Sentence Encoder for Semantic Retrieval, arXiv:1907.04307, 2019. a
Zade, H., Shah, K., Rangarajan, V., Kshirsagar, P., Imran, M., and Starbird,
K.: From Situational Awareness to Actionability: Towards Improving the
Utility of Social Media Data for Crisis Response, Proc. ACM Hum.-Comput.
Interact., 2, 1–18, https://doi.org/10.1145/3274464, 2018.
a
Zhang, Y. and Eick, C. F.: Tracking Events in Twitter by Combining an LDA-Based
Approach and a Density-Contour Clustering Approach, International Journal of
Semantic Computing, 13, 87–110, https://doi.org/10.1142/S1793351X19400051, 2019. a
Zheng, X., Sun, A., Wang, S., and Han, J.: Semi-Supervised Event-related Tweet
Identification with Dynamic Keyword Generation, in: Proceedings of the 2017
ACM CIKM, ACM, 6–10 November 2017, Singapore, 2017. a
Short summary
Messages on social media can be an important source of information during crisis situations. This article reviews approaches for the reliable detection of informative messages in a flood of data. We demonstrate the varying goals of these approaches and present existing data sets. We then compare approaches based (1) on keyword and location filtering, (2) on crowdsourcing, and (3) on machine learning. We also point out challenges and suggest future research.
Messages on social media can be an important source of information during crisis situations....
Altmetrics
Final-revised paper
Preprint