Articles | Volume 20, issue 12
https://doi.org/10.5194/nhess-20-3455-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-20-3455-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Dec 2020
Research article |
| 16 Dec 2020
| 16 Dec 2020
INSPIRE standards as a framework for artificial intelligence applications: a landslide example
Gioachino Roberti
CORRESPONDING AUTHOR
Minerva Intelligence Inc., 301–850 West Hastings Street, Vancouver, British Columbia, Canada V6C 1E1
Jacob McGregor
Sharon Lam
David Bigelow
Blake Boyko
Chris Ahern
Victoria Wang
Bryan Barnhart
Clinton Smyth
David Poole
Minerva Intelligence Inc., 301–850 West Hastings Street, Vancouver, British Columbia, Canada V6C 1E1
Computer Science department, University of British Columbia, Vancouver, British Columbia, Canada V6T 1Z4
Stephen Richard
Minerva Intelligence Inc., 301–850 West Hastings Street, Vancouver, British Columbia, Canada V6C 1E1
Related subject area
Landslides and Debris Flows Hazards
Characteristics and causes of natural and human-induced landslides in a tropical mountainous region: the rift flank west of Lake Kivu (Democratic Republic of the Congo)
Spatio-temporal analysis of slope-type debris flow activity in Horlachtal, Austria, based on orthophotos and lidar data since 1947
Assessing the relationship between weather conditions and rockfall using terrestrial laser scanning to improve risk management
Using principal component analysis to incorporate multi-layer soil moisture information in hydrometeorological thresholds for landslide prediction: an investigation based on ERA5-Land reanalysis data
Assessing uncertainties in landslide susceptibility predictions in a changing environment (Styrian Basin, Austria)
Debris-flow surges of a very active alpine torrent: a field database
Brief communication: An autonomous UAV for catchment-wide monitoring of a debris flow torrent
How volcanic stratigraphy constrains headscarp collapse scenarios: the Samperre cliff case study (Martinique island, Lesser Antilles)
Landslide susceptibility assessment in the rocky coast subsystem of Essaouira, Morocco
Landsifier v1.0: a Python library to estimate likely triggers of mapped landslides
Timing landslide and flash flood events from SAR satellite: a regionally applicable methodology illustrated in African cloud-covered tropical environments
Potential of satellite-derived hydro-meteorological information for landslide initiation thresholds in Rwanda
Instantaneous LEM back-analyses of major rockslides triggered during the 2016–2017 Central Italy seismic sequence
Earthquake-induced landslides in Haiti: analysis of seismotectonic and possible climatic influences
Pre-collapse motion of the February 2021 Chamoli rock–ice avalanche, Indian Himalaya
Physically based modeling of co-seismic landslide, debris flow, and flood cascade
Finite-hillslope analysis of landslides triggered by excess pore water pressure: the roles of atmospheric pressure and rainfall infiltration during typhoons
Estimating global landslide susceptibility and its uncertainty through ensemble modeling
Terrain visibility impact on the preparation of landslide inventories: a practical example in Darjeeling district (India)
Using Sentinel-1 radar amplitude time series to constrain the timings of individual landslides: a step towards understanding the controls on monsoon-triggered landsliding
Introducing SlideforMAP: a probabilistic finite slope approach for modelling shallow-landslide probability in forested situations
Augmentation of WRF-Hydro to simulate overland-flow- and streamflow-generated debris flow susceptibility in burn scars
Sensitivity analysis of a built environment exposed to debris flow impacts with 3-D numerical simulations
Rainfall thresholds estimation for shallow landslides in Peru from gridded daily data
Antecedent rainfall as a critical factor for the triggering of short-lived debris flows in arid regions
What drives landslide risk? Disaggregating risk analyses, an example from the Franz Josef Glacier and Fox Glacier valleys, New Zealand
Geographic information system models with fuzzy logic for susceptibility maps of debris flow using multiple types of parameters: a case study in Pinggu District of Beijing, China
Spatial assessment of probable recharge areas – investigating the hydrogeological controls of an active deep-seated gravitational slope deformation
Rainfall-induced landslide early warning system based on corrected mesoscale numerical models: an application for the southern Andes
Quantification of meteorological conditions for rockfall triggers in Germany
Debris flow velocity and volume estimations based on seismic data
Multi-event assessment of typhoon-triggered landslide susceptibility in the Philippines
Integration of observed and model-derived groundwater levels in landslide threshold models in Rwanda
Landslides caught on seismic networks and satellite radars
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Assessing the importance of conditioning factor selection in landslide susceptibility for the province of Belluno (region of Veneto, northeastern Italy)
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Insights from the topographic characteristics of a large global catalog of rainfall-induced landslide event inventories
Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine
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Rapid assessment of abrupt urban mega-gully and landslide events with structure-from-motion photogrammetric techniques validates link to water resources infrastructure failures in an urban periphery
Automated determination of landslide locations after large trigger events: advantages and disadvantages compared to manual mapping
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A modeling methodology to study the tributary-junction alluvial fan connectivity during a debris flow event
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Main Ethiopian Rift landslides formed in contrasting geological settings and climatic conditions
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Integrating empirical models and satellite radar can improve landslide detection for emergency response
Jean-Claude Maki Mateso, Charles L. Bielders, Elise Monsieurs, Arthur Depicker, Benoît Smets, Théophile Tambala, Luc Bagalwa Mateso, and Olivier Dewitte
Nat. Hazards Earth Syst. Sci., 23, 643–666, https://doi.org/10.5194/nhess-23-643-2023, https://doi.org/10.5194/nhess-23-643-2023, 2023
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This research highlights the importance of human activities on the occurrence of landslides and the need to consider this context when studying hillslope instability patterns in regions under anthropogenic pressure. Also, this study highlights the importance of considering the timing of landslides and hence the added value of using historical information for compiling an inventory.
Jakob Rom, Florian Haas, Tobias Heckmann, Moritz Altmann, Fabian Fleischer, Camillo Ressl, Sarah Betz-Nutz, and Michael Becht
Nat. Hazards Earth Syst. Sci., 23, 601–622, https://doi.org/10.5194/nhess-23-601-2023, https://doi.org/10.5194/nhess-23-601-2023, 2023
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In this study, an area-wide slope-type debris flow record has been established for Horlachtal, Austria, since 1947 based on historical and recent remote sensing data. Spatial and temporal analyses show variations in debris flow activity in space and time in a high-alpine region. The results can contribute to a better understanding of past slope-type debris flow dynamics in the context of extreme precipitation events and their possible future development.
Tom Birien and Francis Gauthier
Nat. Hazards Earth Syst. Sci., 23, 343–360, https://doi.org/10.5194/nhess-23-343-2023, https://doi.org/10.5194/nhess-23-343-2023, 2023
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On highly fractured rockwalls such as those found in northern Gaspésie, most rockfalls are triggered by weather conditions. This study highlights that in winter, rockfall frequency is 12 times higher during a superficial thaw than during a cold period in which temperature remains below 0 °C. In summer, rockfall frequency is 22 times higher during a heavy rainfall event than during a mainly dry period. This knowledge could be used to implement a risk management strategy.
Nunziarita Palazzolo, David J. Peres, Enrico Creaco, and Antonino Cancelliere
Nat. Hazards Earth Syst. Sci., 23, 279–291, https://doi.org/10.5194/nhess-23-279-2023, https://doi.org/10.5194/nhess-23-279-2023, 2023
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We propose an approach exploiting PCA to derive hydrometeorological landslide-triggering thresholds using multi-layered soil moisture data from ERA5-Land reanalysis. Comparison of thresholds based on single- and multi-layered soil moisture information provides a means to identify the significance of multi-layered data for landslide triggering in a region. In Sicily, the proposed approach yields thresholds with a higher performance than traditional precipitation-based ones (TSS = 0.71 vs. 0.50).
Raphael Knevels, Helene Petschko, Herwig Proske, Philip Leopold, Aditya N. Mishra, Douglas Maraun, and Alexander Brenning
Nat. Hazards Earth Syst. Sci., 23, 205–229, https://doi.org/10.5194/nhess-23-205-2023, https://doi.org/10.5194/nhess-23-205-2023, 2023
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In summer 2009 and 2014, rainfall events occurred in the Styrian Basin (Austria), triggering thousands of landslides. Landslide storylines help to show potential future changes under changing environmental conditions. The often neglected uncertainty quantification was the aim of this study. We found uncertainty arising from the landslide model to be of the same order as climate scenario uncertainty. Understanding the dimensions of uncertainty is crucial for allowing informed decision-making.
Suzanne Lapillonne, Firmin Fontaine, Frédéric Liebault, Vincent Richefeu, and Guillaume Piton
EGUsphere, https://doi.org/10.5194/egusphere-2022-1297, https://doi.org/10.5194/egusphere-2022-1297, 2023
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Debris flow are fast flows most often found in torrential watersheds. They are composed of two phases: a liquid phase which can be mud-like and a granular phase, including large boulders, which are transported along with the flow. Due to their destructive nature, accessing features of the flow, such as velocity and flow height is difficult. We present a protocol to analyze debris flow data and results on the Réal torrent in France. These results will help experts for the design of models.
Fabian Walter, Elias Hodel, Erik S. Mannerfelt, Kristen Cook, Michael Dietze, Livia Estermann, Michaela Wenner, Daniel Farinotti, Martin Fengler, Lukas Hammerschmidt, Flavia Hänsli, Jacob Hirschberg, Brian McArdell, and Peter Molnar
Nat. Hazards Earth Syst. Sci., 22, 4011–4018, https://doi.org/10.5194/nhess-22-4011-2022, https://doi.org/10.5194/nhess-22-4011-2022, 2022
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Debris flows are dangerous sediment–water mixtures in steep terrain. Their formation takes place in poorly accessible terrain where instrumentation cannot be installed. Here we propose to monitor such source terrain with an autonomous drone for mapping sediments which were left behind by debris flows or may contribute to future events. Short flight intervals elucidate changes of such sediments, providing important information for landscape evolution and the likelihood of future debris flows.
Marc Peruzzetto, Yoann Legendre, Aude Nachbaur, Thomas J. B. Dewez, Yannick Thiery, Clara Levy, and Benoit Vittecoq
Nat. Hazards Earth Syst. Sci., 22, 3973–3992, https://doi.org/10.5194/nhess-22-3973-2022, https://doi.org/10.5194/nhess-22-3973-2022, 2022
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Volcanic edifices result from successive construction and dismantling phases. Thus, the geological units forming volcanoes display complex geometries. We show that such geometries can be reconstructed thanks to aerial views, topographic surveys and photogrammetric models. In our case study of the Samperre cliff (Martinique, Lesser Antilles), it allows us to link destabilizations from a rocky cliff to the existence of a filled paleo-valley and estimate a potentially unstable volume.
Abdellah Khouz, Jorge Trindade, Sérgio C. Oliveira, Fatima El Bchari, Blaid Bougadir, Ricardo A. C. Garcia, and Mourad Jadoud
Nat. Hazards Earth Syst. Sci., 22, 3793–3814, https://doi.org/10.5194/nhess-22-3793-2022, https://doi.org/10.5194/nhess-22-3793-2022, 2022
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The aim of this study was to assess the landslide susceptibility of the rocky coast of Essaouira using the information value model. The resulting susceptibility maps could be used for both environmental protection and general planning of future development activities.
Kamal Rana, Nishant Malik, and Ugur Ozturk
Nat. Hazards Earth Syst. Sci., 22, 3751–3764, https://doi.org/10.5194/nhess-22-3751-2022, https://doi.org/10.5194/nhess-22-3751-2022, 2022
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The landslide hazard models assist in mitigating losses due to landslides. However, these models depend on landslide databases, which often have missing triggering information, rendering these databases unusable for landslide hazard models. In this work, we developed a Python library, Landsifier, consisting of three different methods to identify the triggers of landslides. These methods can classify landslide triggers with high accuracy using only a landslide polygon shapefile as an input.
Axel A. J. Deijns, Olivier Dewitte, Wim Thiery, Nicolas d'Oreye, Jean-Philippe Malet, and François Kervyn
Nat. Hazards Earth Syst. Sci., 22, 3679–3700, https://doi.org/10.5194/nhess-22-3679-2022, https://doi.org/10.5194/nhess-22-3679-2022, 2022
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Landslides and flash floods are rainfall-induced processes that often co-occur and interact, generally very quickly. In mountainous cloud-covered environments, determining when these processes occur remains challenging. We propose a regional methodology using open-access satellite radar images that allow for the timing of landslide and flash floods events, in the contrasting landscapes of tropical Africa, with an accuracy of up to a few days. The methodology shows potential for transferability.
Judith Uwihirwe, Alessia Riveros, Hellen Wanjala, Jaap Schellekens, Frederiek Sperna Weiland, Markus Hrachowitz, and Thom A. Bogaard
Nat. Hazards Earth Syst. Sci., 22, 3641–3661, https://doi.org/10.5194/nhess-22-3641-2022, https://doi.org/10.5194/nhess-22-3641-2022, 2022
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This study compared gauge-based and satellite-based precipitation products. Similarly, satellite- and hydrological model-derived soil moisture was compared to in situ soil moisture and used in landslide hazard assessment and warning. The results reveal the cumulative 3 d rainfall from the NASA-GPM to be the most effective landslide trigger. The modelled antecedent soil moisture in the root zone was the most informative hydrological variable for landslide hazard assessment and warning in Rwanda.
Luca Verrucci, Giovanni Forte, Melania De Falco, Paolo Tommasi, Giuseppe Lanzo, Kevin W. Franke, and Antonio Santo
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2022-256, https://doi.org/10.5194/nhess-2022-256, 2022
Revised manuscript accepted for NHESS
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Stability analyses in static and seismic conditions were performed on four rockslides occurred during the main shocks of the 2016–2017 Central Italy seismic sequence. These results also indicate that specific structural features of the slope must carefully be accounted for in evaluating potential hazard on r transportation infrastructures in mountainous regions.
Hans-Balder Havenith, Kelly Guerrier, Romy Schlögel, Anika Braun, Sophia Ulysse, Anne-Sophie Mreyen, Karl-Henry Victor, Newdeskarl Saint-Fleur, Léna Cauchie, Dominique Boisson, and Claude Prépetit
Nat. Hazards Earth Syst. Sci., 22, 3361–3384, https://doi.org/10.5194/nhess-22-3361-2022, https://doi.org/10.5194/nhess-22-3361-2022, 2022
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We present a new landslide inventory for the 2021, M 7.2, Haiti, earthquake. We compare characteristics of this inventory with those of the 2010 seismically induced landslides, highlighting the much larger total area of 2021 landslides. This fact could be related to the larger earthquake magnitude in 2021, to the more central location of the fault segment ruptured in 2021 with respect to coastal zones, and/or to possible climatic preconditioning of slope failures in the 2021 affected area.
Maximillian Van Wyk de Vries, Shashank Bhushan, Mylène Jacquemart, César Deschamps-Berger, Etienne Berthier, Simon Gascoin, David E. Shean, Dan H. Shugar, and Andreas Kääb
Nat. Hazards Earth Syst. Sci., 22, 3309–3327, https://doi.org/10.5194/nhess-22-3309-2022, https://doi.org/10.5194/nhess-22-3309-2022, 2022
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On 7 February 2021, a large rock–ice avalanche occurred in Chamoli, Indian Himalaya. The resulting debris flow swept down the nearby valley, leaving over 200 people dead or missing. We use a range of satellite datasets to investigate how the collapse area changed prior to collapse. We show that signs of instability were visible as early 5 years prior to collapse. However, it would likely not have been possible to predict the timing of the event from current satellite datasets.
Bastian van den Bout, Chenxiao Tang, Cees van Westen, and Victor Jetten
Nat. Hazards Earth Syst. Sci., 22, 3183–3209, https://doi.org/10.5194/nhess-22-3183-2022, https://doi.org/10.5194/nhess-22-3183-2022, 2022
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Natural hazards such as earthquakes, landslides, and flooding do not always occur as stand-alone events. After the 2008 Wenchuan earthquake, a co-seismic landslide blocked a stream in Hongchun. Two years later, a debris flow breached the material, blocked the Min River, and resulted in flooding of a small town. We developed a multi-process model that captures the full cascade. Despite input and process uncertainties, probability of flooding was high due to topography and trigger intensities.
Lucas Pelascini, Philippe Steer, Maxime Mouyen, and Laurent Longuevergne
Nat. Hazards Earth Syst. Sci., 22, 3125–3141, https://doi.org/10.5194/nhess-22-3125-2022, https://doi.org/10.5194/nhess-22-3125-2022, 2022
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Landslides represent a major natural hazard and are often triggered by typhoons. We present a new 2D model computing the respective role of rainfall infiltration, atmospheric depression and groundwater in slope stability during typhoons. The results show rainfall is the strongest factor of destabilisation. However, if the slope is fully saturated, near the toe of the slope or during the wet season, rainfall infiltration is limited and atmospheric pressure change can become the dominant factor.
Anne Felsberg, Jean Poesen, Michel Bechtold, Matthias Vanmaercke, and Gabriëlle J. M. De Lannoy
Nat. Hazards Earth Syst. Sci., 22, 3063–3082, https://doi.org/10.5194/nhess-22-3063-2022, https://doi.org/10.5194/nhess-22-3063-2022, 2022
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In this study we assessed global landslide susceptibility at the coarse 36 km spatial resolution of global satellite soil moisture observations to prepare for a subsequent combination of the two. Specifically, we focus therefore on the susceptibility of hydrologically triggered landslides. We introduce ensemble techniques, common in, for example, meteorology but not yet in the landslide community, to retrieve reliable estimates of the total prediction uncertainty.
Txomin Bornaetxea, Ivan Marchesini, Sumit Kumar, Rabisankar Karmakar, and Alessandro Mondini
Nat. Hazards Earth Syst. Sci., 22, 2929–2941, https://doi.org/10.5194/nhess-22-2929-2022, https://doi.org/10.5194/nhess-22-2929-2022, 2022
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One cannot know if there is a landslide or not in an area that one has not observed. This is an obvious statement, but when landslide inventories are obtained by field observation, this fact is seldom taken into account. Since fieldwork campaigns are often done following the roads, we present a methodology to estimate the visibility of the terrain from the roads, and we demonstrate that fieldwork-based inventories are underestimating landslide density in less visible areas.
Katy Burrows, Odin Marc, and Dominique Remy
Nat. Hazards Earth Syst. Sci., 22, 2637–2653, https://doi.org/10.5194/nhess-22-2637-2022, https://doi.org/10.5194/nhess-22-2637-2022, 2022
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The locations of triggered landslides following a rainfall event can be identified in optical satellite images. However cloud cover associated with the rainfall means that these images cannot be used to identify landslide timing. Timings of landslides triggered during long rainfall events are often unknown. Here we present methods of using Sentinel-1 satellite radar data, acquired every 12 d globally in all weather conditions, to better constrain the timings of rainfall-triggered landslides.
Feiko Bernard van Zadelhoff, Adel Albaba, Denis Cohen, Chris Phillips, Bettina Schaefli, Luuk Dorren, and Massimiliano Schwarz
Nat. Hazards Earth Syst. Sci., 22, 2611–2635, https://doi.org/10.5194/nhess-22-2611-2022, https://doi.org/10.5194/nhess-22-2611-2022, 2022
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Shallow landslides pose a risk to people, property and infrastructure. Assessment of this hazard and the impact of protective measures can reduce losses. We developed a model (SlideforMAP) that can assess the shallow-landslide risk on a regional scale for specific rainfall events. Trees are an effective and cheap protective measure on a regional scale. Our model can assess their hazard reduction down to the individual tree level.
Chuxuan Li, Alexander L. Handwerger, Jiali Wang, Wei Yu, Xiang Li, Noah J. Finnegan, Yingying Xie, Giuseppe Buscarnera, and Daniel E. Horton
Nat. Hazards Earth Syst. Sci., 22, 2317–2345, https://doi.org/10.5194/nhess-22-2317-2022, https://doi.org/10.5194/nhess-22-2317-2022, 2022
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In January 2021 a storm triggered numerous debris flows in a wildfire burn scar in California. We use a hydrologic model to assess debris flow susceptibility in pre-fire and postfire scenarios. Compared to pre-fire conditions, postfire conditions yield dramatic increases in peak water discharge, substantially increasing debris flow susceptibility. Our work highlights the hydrologic model's utility in investigating and potentially forecasting postfire debris flows at regional scales.
Xun Huang, Zhijian Zhang, and Guoping Xiang
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2022-173, https://doi.org/10.5194/nhess-2022-173, 2022
Revised manuscript accepted for NHESS
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A sensitivity analysis on the building impact force resulting from the representative built environment parameters is executed through FLOW-3D model. The surrounding buildings’ properties, especially the azimuthal angle, have been confirmed to play significant roles in determining the peak impact forces. The single and combined effect of built environments are analyzed in detail. This will improve the understanding of vulnerability assessment and migration design against debris flow hazards.
Carlos Millán-Arancibia and Waldo Lavado-Casimiro
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2022-199, https://doi.org/10.5194/nhess-2022-199, 2022
Revised manuscript accepted for NHESS
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This study is the first approximation of regional rainfall thresholds that trigger shallow landslides in Peru. This research was generated from a gridded precipitation data and landslide inventory. The analysis showed that the threshold based on the combination of mean daily intensity-duration variables gives the best results for separating rainfall events that generate landslides. Through this work, is demonstrate the potential of thresholds for landslide monitoring at the regional level.
Shalev Siman-Tov and Francesco Marra
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2022-184, https://doi.org/10.5194/nhess-2022-184, 2022
Revised manuscript accepted for NHESS
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Debris flows represent a threat to infrastructures and population. In arid areas, they are observed when heavy rainfall hits steep slopes with sediments. Here, we use digital surface models and radar rainfall data to detect and characterize the triggering and non-triggering rainfall conditions. We find that rainfall intensity alone is insufficient to explain the triggering. We suggest that antecedent rainfall could represent a critical factor for debris flow triggering in arid regions.
Saskia de Vilder, Chris Massey, Biljana Lukovic, Tony Taig, and Regine Morgenstern
Nat. Hazards Earth Syst. Sci., 22, 2289–2316, https://doi.org/10.5194/nhess-22-2289-2022, https://doi.org/10.5194/nhess-22-2289-2022, 2022
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This study calculates the fatality risk posed by landslides while visiting Franz Josef Glacier and Fox Glacier valleys, New Zealand, for nine different scenarios, where the variables of the risk equation were adjusted to determine the range in risk values and associated uncertainty. The results show that it is important to consider variable inputs that change through time, such as the increasing probability of an earthquake and the impact of climate change on landslide characteristics.
Yiwei Zhang, Jianping Chen, Qing Wang, Chun Tan, Yongchao Li, Xiaohui Sun, and Yang Li
Nat. Hazards Earth Syst. Sci., 22, 2239–2255, https://doi.org/10.5194/nhess-22-2239-2022, https://doi.org/10.5194/nhess-22-2239-2022, 2022
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The disaster prevention and mitigation of debris flow is a very important scientific problem. Our model is based on geographic information system (GIS), combined with grey relational, data-driven and fuzzy logic methods. Through our results, we believe that the streamlining of factors and scientific classification should attract attention from other researchers to optimize a model. We also propose a good perspective to make better use of the watershed feature parameters.
Jan Pfeiffer, Thomas Zieher, Jan Schmieder, Thom Bogaard, Martin Rutzinger, and Christoph Spötl
Nat. Hazards Earth Syst. Sci., 22, 2219–2237, https://doi.org/10.5194/nhess-22-2219-2022, https://doi.org/10.5194/nhess-22-2219-2022, 2022
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The activity of slow-moving deep-seated landslides is commonly governed by pore pressure variations within the shear zone. Groundwater recharge as a consequence of precipitation therefore is a process regulating the activity of landslides. In this context, we present a highly automated geo-statistical approach to spatially assess groundwater recharge controlling the velocity of a deep-seated landslide in Tyrol, Austria.
Ivo Fustos-Toribio, Nataly Manque-Roa, Daniel Vásquez Antipan, Mauricio Hermosilla Sotomayor, and Viviana Letelier Gonzalez
Nat. Hazards Earth Syst. Sci., 22, 2169–2183, https://doi.org/10.5194/nhess-22-2169-2022, https://doi.org/10.5194/nhess-22-2169-2022, 2022
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We develop for the first time a rainfall-induced landslide early warning system for the south of Chile. We used forecast precipitation values at different scales using mesoscale models to evaluate the probability of landslides using statistical models. We showed the feasibility of implementing these models in future, supporting stakeholders and decision-makers.
Katrin M. Nissen, Stefan Rupp, Thomas M. Kreuzer, Björn Guse, Bodo Damm, and Uwe Ulbrich
Nat. Hazards Earth Syst. Sci., 22, 2117–2130, https://doi.org/10.5194/nhess-22-2117-2022, https://doi.org/10.5194/nhess-22-2117-2022, 2022
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A statistical model is introduced which quantifies the influence of individual potential triggering factors and their interactions on rockfall probability in central Europe. The most important factor is daily precipitation, which is most effective if sub-surface moisture levels are high. Freeze–thaw cycles in the preceding days can further increase the rockfall hazard. The model can be applied to climate simulations in order to investigate the effect of climate change on rockfall probability.
Andreas Schimmel, Velio Coviello, and Francesco Comiti
Nat. Hazards Earth Syst. Sci., 22, 1955–1968, https://doi.org/10.5194/nhess-22-1955-2022, https://doi.org/10.5194/nhess-22-1955-2022, 2022
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The estimation of debris flow velocity and volume is a fundamental task for the development of early warning systems and other mitigation measures. This work provides a first approach for estimating the velocity and the total volume of debris flows based on the seismic signal detected with simple, low-cost geophones installed along the debris flow channel. The developed method was applied to seismic data collected at three test sites in the Alps: Gadria and Cancia (IT) and Lattenbach (AT).
Joshua N. Jones, Georgina L. Bennett, Claudia Abancó, Mark M. A. Matera, and Fibor J. Tan
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2022-88, https://doi.org/10.5194/nhess-2022-88, 2022
Revised manuscript accepted for NHESS
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We modelled where landslides occur in the Philippines using landslide data from three typhoon events in 2009, 2018 and 2019. These models show where landslides occurred within the landscape. By comparing the different models, we found that the 2019 landslides were occurring all across the landscape, whereas the 2009 and 2018 landslides were mostly occurring at specific slope angles and aspects. This shows that landslide susceptibility must be considered variable through space and time.
Judith Uwihirwe, Markus Hrachowitz, and Thom Bogaard
Nat. Hazards Earth Syst. Sci., 22, 1723–1742, https://doi.org/10.5194/nhess-22-1723-2022, https://doi.org/10.5194/nhess-22-1723-2022, 2022
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This research tested the value of regional groundwater level information to improve landslide predictions with empirical models based on the concept of threshold levels. In contrast to precipitation-based thresholds, the results indicated that relying on threshold models exclusively defined using hydrological variables such as groundwater levels can lead to improved landslide predictions due to their implicit consideration of long-term antecedent conditions until the day of landslide occurrence.
Andrea Manconi, Alessandro C. Mondini, and the AlpArray working group
Nat. Hazards Earth Syst. Sci., 22, 1655–1664, https://doi.org/10.5194/nhess-22-1655-2022, https://doi.org/10.5194/nhess-22-1655-2022, 2022
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Information on when, where, and how landslide events occur is the key to building complete catalogues and performing accurate hazard assessments. Here we show a procedure that allows us to benefit from the increased density of seismic sensors installed on ground for earthquake monitoring and from the unprecedented availability of satellite radar data. We show how the procedure works on a recent sequence of landslides that occurred at Piz Cengalo (Swiss Alps) in 2017.
Andrew Mitchell, Sophia Zubrycky, Scott McDougall, Jordan Aaron, Mylène Jacquemart, Johannes Hübl, Roland Kaitna, and Christoph Graf
Nat. Hazards Earth Syst. Sci., 22, 1627–1654, https://doi.org/10.5194/nhess-22-1627-2022, https://doi.org/10.5194/nhess-22-1627-2022, 2022
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Debris flows are complex, surging movements of sediment and water. Discharge observations from well-studied debris-flow channels were used as inputs for a numerical modelling study of the downstream effects of chaotic inflows. The results show that downstream impacts are sensitive to inflow conditions. Inflow conditions for predictive modelling are highly uncertain, and our method provides a means to estimate the potential variability in future events.
Sansar Raj Meena, Silvia Puliero, Kushanav Bhuyan, Mario Floris, and Filippo Catani
Nat. Hazards Earth Syst. Sci., 22, 1395–1417, https://doi.org/10.5194/nhess-22-1395-2022, https://doi.org/10.5194/nhess-22-1395-2022, 2022
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The study investigated the importance of the conditioning factors in predicting landslide occurrences using the mentioned models. In this paper, we evaluated the importance of the conditioning factors (features) in the overall prediction capabilities of the statistical and machine learning algorithms.
Pierpaolo Distefano, David J. Peres, Pietro Scandura, and Antonino Cancelliere
Nat. Hazards Earth Syst. Sci., 22, 1151–1157, https://doi.org/10.5194/nhess-22-1151-2022, https://doi.org/10.5194/nhess-22-1151-2022, 2022
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In the communication, we introduce the use of artificial neural networks (ANNs) for improving the performance of rainfall thresholds for landslide early warning. Results show how ANNs using rainfall event duration and mean intensity perform significantly better than a classical power law based on the same variables. Adding peak rainfall intensity as input to the ANN improves performance even more. This further demonstrates the potentialities of the proposed machine learning approach.
Robert Emberson, Dalia B. Kirschbaum, Pukar Amatya, Hakan Tanyas, and Odin Marc
Nat. Hazards Earth Syst. Sci., 22, 1129–1149, https://doi.org/10.5194/nhess-22-1129-2022, https://doi.org/10.5194/nhess-22-1129-2022, 2022
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Understanding where landslides occur in mountainous areas is critical to support hazard analysis as well as understand landscape evolution. In this study, we present a large compilation of inventories of landslides triggered by rainfall, including several that are described here for the first time. We analyze the topographic characteristics of the landslides, finding consistent relationships for landslide source and deposition areas, despite differences in the inventories' locations.
Alexander L. Handwerger, Mong-Han Huang, Shannan Y. Jones, Pukar Amatya, Hannah R. Kerner, and Dalia B. Kirschbaum
Nat. Hazards Earth Syst. Sci., 22, 753–773, https://doi.org/10.5194/nhess-22-753-2022, https://doi.org/10.5194/nhess-22-753-2022, 2022
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Rapid detection of landslides is critical for emergency response and disaster mitigation. Here we develop a global landslide detection tool in Google Earth Engine that uses satellite radar data to measure changes in the ground surface properties. We find that we can detect areas with high landslide density within days of a triggering event. Our approach allows the broader hazard community to utilize these state-of-the-art data for improved situational awareness of landslide hazards.
Qiwen Lin, Yufeng Wang, Yu Xie, Qiangong Cheng, and Kaifeng Deng
Nat. Hazards Earth Syst. Sci., 22, 639–657, https://doi.org/10.5194/nhess-22-639-2022, https://doi.org/10.5194/nhess-22-639-2022, 2022
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Fracturing and fragmentation of rock blocks are important and universal phenomena during the movement of rock avalanches (large and long-run-out rockslide-debris avalanches). The movement of a fragmenting rock block is simulated by the discrete element method, aiming to quantify the fracturing and fragmentation effect of the block in propagation. The fracturing and fragmentation processes and their influences on energy transformation in the system are described in detail.
Napoleon Gudino-Elizondo, Matthew W. Brand, Trent W. Biggs, Alejandro Hinojosa-Corona, Álvaro Gómez-Gutiérrez, Eddy Langendoen, Ronald Bingner, Yongping Yuan, and Brett F. Sanders
Nat. Hazards Earth Syst. Sci., 22, 523–538, https://doi.org/10.5194/nhess-22-523-2022, https://doi.org/10.5194/nhess-22-523-2022, 2022
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Mass movement hazards in the form of gullies and landslides pose significant risks in urbanizing areas yet are poorly documented. This paper presents observations and modeling of mass movement events over a 5-year period in Tijuana, Mexico. Three major events were observed, and all were linked to water resources infrastructure failures (WRIFs), namely leaks and breaks in water supply pipes. Modeling shows that WRIF-based erosion was also a non-negligible contributor to the total sediment budget.
David G. Milledge, Dino G. Bellugi, Jack Watt, and Alexander L. Densmore
Nat. Hazards Earth Syst. Sci., 22, 481–508, https://doi.org/10.5194/nhess-22-481-2022, https://doi.org/10.5194/nhess-22-481-2022, 2022
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Earthquakes can trigger thousands of landslides, causing severe and widespread damage. Efforts to understand what controls these landslides rely heavily on costly and time-consuming manual mapping from satellite imagery. We developed a new method that automatically detects landslides triggered by earthquakes using thousands of free satellite images. We found that in the majority of cases, it was as skilful at identifying the locations of landslides as the manual maps that we tested it against.
Sohrab Sharifi, Michael T. Hendry, Renato Macciotta, and Trevor Evans
Nat. Hazards Earth Syst. Sci., 22, 411–430, https://doi.org/10.5194/nhess-22-411-2022, https://doi.org/10.5194/nhess-22-411-2022, 2022
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This study is devoted to comparing the effectiveness of three different filters for noise reduction of instruments. It was observed that the Savitzky–Golay and Gaussian-weighted moving average filters outperform the simple moving average. Application of these two filters in real-time landslide monitoring leads to timely detection of acceleration moment and better preservation of information regarding displacement and velocity.
Alex Garcés, Gerardo Zegers, Albert Cabré, Germán Aguilar, Aldo Tamburrino, and Santiago Montserrat
Nat. Hazards Earth Syst. Sci., 22, 377–393, https://doi.org/10.5194/nhess-22-377-2022, https://doi.org/10.5194/nhess-22-377-2022, 2022
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We propose a workflow to model the response of an alluvial fan located in the Atacama Desert during an extreme storm event. For this alluvial fan, five different deposits were identified and associated with different debris flow surges. Using a commercial software program, our workflow concatenates these surges into one model. This study depicts the significance of the mechanical classification of debris flows to reproduce how an alluvial fan controls the tributary–river junction connectivity.
Jim S. Whiteley, Arnaud Watlet, J. Michael Kendall, and Jonathan E. Chambers
Nat. Hazards Earth Syst. Sci., 21, 3863–3871, https://doi.org/10.5194/nhess-21-3863-2021, https://doi.org/10.5194/nhess-21-3863-2021, 2021
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This work summarises the contribution of geophysical imaging methods to establishing and operating local landslide early warning systems, demonstrated through a conceptual framework. We identify developments in geophysical monitoring equipment, the spatiotemporal resolutions of these approaches and methods to translate geophysical to geotechnical information as the primary benefits that geophysics brings to slope-scale early warning.
Vipin Kumar, Léna Cauchie, Anne-Sophie Mreyen, Mihai Micu, and Hans-Balder Havenith
Nat. Hazards Earth Syst. Sci., 21, 3767–3788, https://doi.org/10.5194/nhess-21-3767-2021, https://doi.org/10.5194/nhess-21-3767-2021, 2021
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The SE Carpathians belong to one of the most active seismic regions of Europe. In recent decades, extreme rainfall events have also been common. These natural processes result in frequent landslides, particularly of a debris flow type. Despite such regimes, the region has been little explored to understand the response of the landslides in seismic and rainfall conditions. This study attempts to fill this gap by evaluating landslide responses under seismic and extreme-rainfall regimes.
Karel Martínek, Kryštof Verner, Tomáš Hroch, Leta A. Megerssa, Veronika Kopačková, David Buriánek, Ameha Muluneh, Radka Kalinová, Miheret Yakob, and Muluken Kassa
Nat. Hazards Earth Syst. Sci., 21, 3465–3487, https://doi.org/10.5194/nhess-21-3465-2021, https://doi.org/10.5194/nhess-21-3465-2021, 2021
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This study combines field geological and geohazard mapping with remote sensing data. Geostatistical analysis evaluated precipitation, land use, vegetation density, rock mass strength, and tectonics. Contrasting tectonic and climatic setting of the Main Ethiopian Rift and uplifted Ethiopian Plateau have major impacts on the distribution of landslides.
Lauren Zweifel, Maxim Samarin, Katrin Meusburger, and Christine Alewell
Nat. Hazards Earth Syst. Sci., 21, 3421–3437, https://doi.org/10.5194/nhess-21-3421-2021, https://doi.org/10.5194/nhess-21-3421-2021, 2021
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Mountainous grassland areas can be severely affected by soil erosion, such as by shallow landslides. With an automated mapping approach we are able to locate shallow-landslide sites on aerial images for 10 different study sites across Swiss mountain regions covering a total of 315 km2. Using a statistical model we identify important explanatory variables for shallow-landslide occurrence for the individual sites as well as across all regions, which highlight slope, aspect and terrain roughness.
Ivo Janos Fustos-Toribio, Bastian Morales-Vargas, Marcelo Somos-Valenzuela, Pablo Moreno-Yaeger, Ramiro Muñoz-Ramirez, Ines Rodriguez Araneda, and Ningsheng Chen
Nat. Hazards Earth Syst. Sci., 21, 3015–3029, https://doi.org/10.5194/nhess-21-3015-2021, https://doi.org/10.5194/nhess-21-3015-2021, 2021
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Links between debris flow and volcanic evolution are an open question in the southern Andes. We modelled the catastrophic debris flow using field data, a geotechnical approach and numerical modelling of the Petrohué event (Chile, 2017). Our results indicated new debris-flow-prone zones. Finally, we propose considering connections between volcanoes and debris flow in the southern Andes.
Katy Burrows, David Milledge, Richard J. Walters, and Dino Bellugi
Nat. Hazards Earth Syst. Sci., 21, 2993–3014, https://doi.org/10.5194/nhess-21-2993-2021, https://doi.org/10.5194/nhess-21-2993-2021, 2021
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When cloud cover obscures optical satellite imagery, there are two options remaining for generating information on earthquake-triggered landslide locations: (1) models which predict landslide locations based on, e.g., slope and ground shaking data and (2) satellite radar data, which penetrates cloud cover and is sensitive to landslides. Here we show that the two approaches can be combined to give a more consistent and more accurate model of landslide locations after an earthquake.
Cited articles
Alvioli, M., Marchesini, I., Reichenbach, P., Rossi, M., Ardizzone, F., Fiorucci, F., and Guzzetti, F.: Automatic delineation of geomorphological slope units with r.slopeunits v1.0 and their optimization for landslide susceptibility modeling, Geosci. Model Dev., 9, 3975–3991, https://doi.org/10.5194/gmd-9-3975-2016, 2016. a
Alvioli, M., Guzzetti, F., and Marchesini, I.: Parameter-free delineation of
slope units and terrain subdivision of Italy, Geomorphology, 358, 107124,
https://doi.org/10.1016/j.geomorph.2020.107124, 2020. a
Aristotle: The Categories, 350 BCE. a
Association of Professional Engineers and Geoscientists of British Columbia:
Guidelines for Legislated Landslide Assessments for proposed residencial
development in BC, EGBC, Burnaby, British Columbia, Canada, Tech. Rep. May, 2010. a
Baum, R. L., Savage, W. Z., and Jonathan W., G.: Trigrs – A Fortran Program
for Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability
Analysis, Version 2.0, USGS, Denver, Colorado, United States, Tech. rep., 2008. a
Cetl, V., Nunes De Lima, V., Tomas, R., Lutz, M., D'eugenio, J., Nagy,
A., and Robbrecht, J.: Summary Report on Status of implementation of the
INSPIRE Directive in EU, https://doi.org/10.2760/162895, 2017. a
CGI: GeoSciML, available at: http://www.geosciml.org/ (last access: 26 October 2020), 2003. a
Cho, G. and Crompvoets, J.: The INSPIRE directive: some observations on the
legal framework and implementation, Surv. Rev., 51, 310–317,
https://doi.org/10.1080/00396265.2018.1454686, 2019. a, b
Corominas, J., van Westen, C., Frattini, P., Cascini, L., Malet, J. P.,
Fotopoulou, S., Catani, F., Van Den Eeckhaut, M., Mavrouli, O., Agliardi,
F., Pitilakis, K., Winter, M. G., Pastor, M., Ferlisi, S., Tofani, V.,
Hervás, J., and Smith, J. T.: Recommendations for the quantitative
analysis of landslide risk, B. Eng. Geol. Environ., 73, 209–263, https://doi.org/10.1007/s10064-013-0538-8, 2014. a
Craglia, M. and Campagna, M.: Advanced Regional SDI in Europe: Comparative
cost-benefit evaluation and impact assessment perspectives, International
Journal of Spatial Data Infrastructures Research, 5, 145–167,
https://doi.org/10.2902/1725-0463.2010.05.art6, 2010. a
Dai, F. C., Lee, C. F., and Ngai, Y. Y.: Landslide risk assessment and
management: An overview, Eng. Geol., 64, 65–87,
https://doi.org/10.1016/S0013-7952(01)00093-X, 2002. a
Dieu, T. B. and Gjermundsen, E. F.: Advance Geospatial Artificial Intelligence
for Landslide Modeling, Prediction and Management, ISPRS
Int. Geo.-Inf., Special Issue, available at: https://www.mdpi.com/journal/ijgi/special_issues/AI_landslide?listby=type&view=abstract (last access: October 2020),
2020. a
European Commission: INSPIRE Helsinki 2019,
available at: https://www.inspire-helsinki-2019.fi/ (last access: October 2020), 2019. a
European Parliament and the Council: Commission Regulation (EU) No 1312/2014
of 10 December 2014 amending Regulation (EU) No 1089/2010 implementing
Directive 2007/2/EC of the European Parliament and of the Council as regards
interoperability of spatial data services, Official Journal of the European
Union, 53, 13–34, https://doi.org/10.3000/17252555.L_2010.323.eng, 2014. a
Evans, S. G. and Clague, J. J.: Recent climatic change and catastrophic
geomorphic processes in mountain environments, Geomorphology, 10, 107–128,
https://doi.org/10.1016/0169-555X(94)90011-6, 1994. a, b, c
Facebook: React, available at: https://reactjs.org/ (last access: 26 October 2020), 2013. a
Feranec, J., Soukup, T., Hazeu, G., and Jaffrain, G. (Eds.): European Landscape Dynamics, Boca Raton, CRC Press, https://doi.org/10.1201/9781315372860, 2016. a
Friele, P., Millard, T. H., Mitchell, A., Allstadt, K. E., Menounos, B.,
Geertsema, M., and Clague, J. J.: Observations on the May 2019 Joffre Peak
landslides, British Columbia, Landslides, 17, 913–930, https://doi.org/10.1007/s10346-019-01332-2,
2020. a
Friele, P. A.: Volcanic Landslide Risk Management, Lillooet River Valley, BC:
Start of north and south FSRs to Meager Confluence, Meager Creek and Upper
Lillooet River, Metro Vancouver Squamish District
Ministry of Forests, Lands and Natural Resource Operations, Vancouver, British Columbia, Canada, Tech. rep., 2012. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x
Froude, M. J. and Petley, D. N.: Global fatal landslide occurrence from 2004 to 2016, Nat. Hazards Earth Syst. Sci., 18, 2161–2181, https://doi.org/10.5194/nhess-18-2161-2018, 2018. a
Geertsema, M.: Quick Clay, Springer Netherlands, Dordrecht, 803–804, in: Encyclopedia of Natural Hazards, 2013 edn., edited by: Bobrowsky, P. T., https://doi.org/10.1007/978-1-4020-4399-4_282, 2013. a
Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., and Kagal, L.:
Explaining explanations: An overview of interpretability of machine
learning, Proceedings – 2018 IEEE 5th International Conference on Data
Science and Advanced Analytics, Turin, Italy, DSAA 2018, 80–89,
https://doi.org/10.1109/DSAA.2018.00018, 2019. a
Goodfellow, I., Bengio, Y., and Aaron, C.: Deep Learning, MIT press, Cambridge, Massachusetts, United States, 2016. a
Gruber, S.: Derivation and analysis of a high-resolution estimate of global permafrost zonation, The Cryosphere, 6, 221–233, https://doi.org/10.5194/tc-6-221-2012, 2012. a, b
Guzzetti, F., Carrara, A., Cardinali, M., and Reichenbach, P.: Landslide
hazard evaluation: a review of current techniques and their application in a
multi-scale study, Central Italy, Geomorphology, 13, 1995,
https://doi.org/10.1016/S0169-555X(99)00078-1, 1999. a, b
Guzzetti, F., Mondini, A. C., Cardinali, M., Fiorucci, F., Santangelo, M., and
Chang, K. T.: Landslide inventory maps: New tools for an old problem,
Earth.-Sci. Rev., 112, 42–66, https://doi.org/10.1016/j.earscirev.2012.02.001, 2012. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x
Guzzetti, F., Gariano, S. L., Peruccacci, S., Brunetti, M. T., Marchesini, I.,
Rossi, M., and Melillo, M.: Geographical landslide early warning systems,
Earth-Sci. Rev., 200, 102973, https://doi.org/10.1016/j.earscirev.2019.102973, 2020. a
Hajimoradlou, A., Roberti, G., and Poole, D.: Predicting Landslides Using
Locally Aligned Convolutional Neural Networks, in: Proceedings of the
Twenty-Ninth International Joint Conference on Artificial Intelligence,
{IJCAI-20}, edited by: Bessiere, C., International Joint Conferences on
Artificial Intelligence Organization, Yokohama, Japan, 2020. a, b
Howes, D. E. and Kenk, E.: Terrain Classification System for British
Columbia, Tech. Rep. Version 2, Fisheries Branch Ministry of Environment and
Surveys and Resource Mapping Branch Ministry of Crown Lands Province of
British Columbia, Victoria, British Columbia, Canada, 1997. a, b, c, d, e, f, g, h, i, j, k
Hungr, O., Leroueil, S., and Picarelli, L.: The Varnes classification of
landslide types, an update, Landslides, 11, 167–194,
https://doi.org/10.1007/s10346-013-0436-y, 2014. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z, aa, ab, ac, ad, ae, af, ag, ah, ai, aj, ak, al, am, an, ao, ap, aq, ar, as, at, au, av, aw, ax, ay, az, ba, bb, bc, bd, be, bf, bg
Hykes, S.: Docker, available at: https://www.docker.com/ (last access: 26 October 2020), 2013. a
INSPIRE Thematic Working Group Natural Risk Zones: D2.8.III.12 Data
Specification on Natural Risk Zones – Technical Guidelines, Tech. Rep.
March 2007, European Commission Joint Research Centre, Ispra, Italy, 2013. a
ISA: Re3gistry software 1.3,
available at: https://ec.europa.eu/isa2/solutions/re3gistry_en (last access: 26 October 2020), 2016. a
Jackson Jr, L. E.: Recommendation for adding logging, logging road,
wildfire, and morphometric parameters to the soil-slide model, Tech. rep.,
available at: https://italy.minervageo.com/wp-content/uploads/2020/07/Minerva_Jackson2019.pdf (last access: 26 October 2020),
2019. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z, aa, ab, ac, ad, ae, af, ag, ah
Jakob, M.: A size classification for debris flows, Eng. Geol., 79,
151–161, https://doi.org/10.1016/j.enggeo.2005.01.006, 2005. a, b, c, d
Marcus, G.: Deep Learning: A Critical Appraisal, 1–27, arxiv [preprint], arxiv:1801.00631, 2018. a
Marcus, G. and Davis, E.: Rebooting AI: Building Artificial Intelligence We
Can Trust, Knopf Doubleday Publishing Group, New York, NY, United States, 2019. a
McDougall, S.: 2014 Canadian Geotechnical Colloquium: Landslide runout
analysis – current practice and challenges, Can. Geotech. J.,
54, 605–620, https://doi.org/10.1139/cgj-2016-0104, 2016. a
Mergili, M., Marchesini, I., Alvioli, M., Metz, M., Schneider-Muntau, B., Rossi, M., and Guzzetti, F.: A strategy for GIS-based 3-D slope stability modelling over large areas, Geosci. Model Dev., 7, 2969–2982, https://doi.org/10.5194/gmd-7-2969-2014, 2014. a
Mergili, M., Fischer, J.-T., Krenn, J., and Pudasaini, S. P.: r.avaflow v1, an advanced open-source computational framework for the propagation and interaction of two-phase mass flows, Geosci. Model Dev., 10, 553–569, https://doi.org/10.5194/gmd-10-553-2017, 2017. a
MetaCarta: OpensLayer, available at: https://openlayers.org/ (last access: 26 October 2020), 2005. a
Mijić, N. and Bartha, G.: Infrastructure for Spatial Information in
European Community (INSPIRE) Through the Time from 2007. Until 2017, Springer, Cham, 60, 34–42, https://doi.org/10.1007/978-3-030-02577-9_5, 2018. a
Minerva Intelligence: Aristotelean Class Editor – ACE,
available at: https://ace.minervaintelligence.com/ (last access: 26 October 2020),
2019d. a
Minerva Intelligence: Sea to Sky hazards,
available at: https://www.minervageohazards.com/ (last access: 26 October 2020), 2019f. a
Minerva Intelligence: Minerva GAIA: Veneto Landslides, available at: https://map.italy.minervageohazards.com/ (last access: October 2020), 2019g. a
Ministero dell'Ambiente e della Tutela del Territorio e del Mare: Geoportale Nazionale, available at: http://www.pcn.minambiente.it/mattm/ (last access: 26 October 2020), 2017. a
Napolitano, E., Marchesini, I., Salvati, P., Donnini, M., Bianchi, C., and
Guzzetti, F.: LAND-deFeND – An innovative database structure for
landslides and floods and their consequences, J. Environ. Manage., 207, 203–218, https://doi.org/10.1016/j.jenvman.2017.11.022, 2018. a
Newman, J. P., Maier, H. R., Riddell, G. A., Zecchin, A. C., Daniell, J. E.,
Schaefer, A. M., van Delden, H., Khazai, B., O'Flaherty, M. J., and Newland,
C. P.: Review of literature on decision support systems for natural hazard
risk reduction: Current status and future research directions, Environ. Modell. Softw., 96, 378–409, https://doi.org/10.1016/j.envsoft.2017.06.042, 2017. a
Phengsuwan, J., Shah, T., James, P., Thakker, D., Barr, S., and Ranjan, R.:
Ontology-based discovery of time-series data sources for landslide early
warning system, Computing, 102, 745–763, https://doi.org/10.1007/s00607-019-00730-7, 2019. a
Poole, D., Smyth, C., and Sharma, R.: Ontology Design for Scientific
Theories That Make Probabilistic Predictions, IEEE Intell. Syst., 24,
27–36, 2009. a
Pudasaini, S. P.: A general two-phase debris flow model, J. Geophys. Res.-Earth, 117, 1–28, https://doi.org/10.1029/2011JF002186,
2012. a
Refraction Researtch: PostGIS, available at: https://postgis.net/ (last access: 26 October 2020), 2001. a
Regione del Veneto: Geoportale, available at: https://idt2.regione.veneto.it/, last access: 26 October 2020. a
Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., and Guzzetti, F.: A
review of statistically-based landslide susceptibility models, Earth-Sci. Rev., 180, 60–91, https://doi.org/10.1016/j.earscirev.2018.03.001, 2018. a, b, c, d
Segoni, S., Tofani, V., Rosi, A., Catani, F., and Casagli, N.: Combination of
Rainfall Thresholds and Susceptibility Maps for Dynamic Landslide Hazard
Assessment at Regional Scale, Front. Earth Sci., 6, p. 85,
https://doi.org/10.3389/feart.2018.00085, a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x
Sermet, Y. and Demir, I.: Towards an information centric flood ontology for
information management and communication, Earth Sci. Inform., 12,
541–551, https://doi.org/10.1007/s12145-019-00398-9, 2019. a
Shapiro, S. C.: Encyclopedia of Artificial Intelligence, John Wiley & Sons,
Inc., New York, USA, 2nd edn., 1992. a
Sharma, R., Poole, D., and Smyth, C.: A framework for ontologically-grounded
probabilistic matching, Int. J. Approx. Reason,
51, 240–262, https://doi.org/10.1016/j.ijar.2009.05.007, 2010. a, b
Smith, B.: Ontology, in: Blackwell Guide to the Philosophy of Computing and
Information, edited by: Floridi, L., chap. 11, 155–166, Blackwell,
Oxford, 2003. a
Tarquini, S., Isola, I., Favalli, M., and Battistini, A.: TINITALY, a digital elevation model of Italy with a 10 m-cell size (Version 1.0), Data set, Istituto Nazionale di Geofisica e Vulcanologia (INGV), https://doi.org/10.13127/TINITALY/1.0, 2007. a
The Open Planning Project: Geoserver,
available at: http://geoserver.org/ (last access: 26 October 2020), 2001. a
Tomas, R., Harrison, M., Barredo, J. I., Thomas, F., Llorente Isidro, M.,
Pfeiffer, M., and Čerba, O.: Towards a cross-domain interoperable
framework for natural hazards and disaster risk reduction information,
Nat. Hazards, 78, 1545–1563, https://doi.org/10.1007/s11069-015-1786-7, 2015. a, b, c
Van Den Eeckhaut, M. and Hervás, J.: State of the art of national
landslide databases in Europe and their potential for assessing landslide
susceptibility, hazard and risk, Geomorphology, 139-140, 545–558, https://doi.org/10.1016/j.geomorph.2011.12.006, 2012. a, b
Van Den Eeckhaut, M., Hervas, J., and Montanarella, L.: Landslide Databases
in Europe: Analysis and Recommendations for Interoperability and
Harmonisation, in: Landslide Science and Practice, edited by: Margottini, C., Canuti, P., and Sassa, K., vol. 1, 243–247, Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-642-31325-7_4, 2013. a
W3C Working Group: RDF 1.1 Primer,
available at: https://www.w3.org/TR/rdf11-primer (last access: 26 October 2020), 2014. a
WeTransform: Hale Connect,
available at: https://www.wetransform.to/products/haleconnect/ (last access: 26 October 2020), 2014. a
Wilford, D. J., Sakals, M. E., Innes, J. L., Sidle, R. C., and Bergerud, W. A.:
Recognition of debris flow, debris flood and flood hazard through watershed
morphometrics, Landslides, 1, 61–66, https://doi.org/10.1007/s10346-003-0002-0, 2004. a
Short summary
We show how INSPIRE, the European initiative to standardize data across borders, can be used to produce explainable AI-based applications. We do so by producing landslide susceptibility maps for the Veneto region in Italy. EU countries are mandated by law to implement the INSPIRE data framework by 2021, but they are aligning and serving INSPIRE data at a slow pace. Our paper can provide a boost to INSPIRE implementation as it shows the value of standardized data.
We show how INSPIRE, the European initiative to standardize data across borders, can be used to...
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