Groundbreaking technologies, big data, and innovation for disaster risk modelling and reduction
Groundbreaking technologies, big data, and innovation for disaster risk modelling and reduction
Editor(s): Rui Figueiredo, Kai Schröter, Mario Lloyd Virgilio Martina, and Carmine Galasso
Global losses due to natural hazards have shown an increasing trend over the last decades, which is expected to continue due to growing exposure in disaster-prone areas and the effects of climate change. In response, recent years have seen greater worldwide commitment to reducing disaster risk. Working towards this end requires the implementation of increasingly effective disaster risk management (DRM) strategies. These must necessarily be supported by reliable estimates of risk and loss before, during, and after a disaster. In this context, innovation plays a key role.

This special issue focuses on the development and application of groundbreaking technologies, big data, and innovative modelling approaches for disaster risk assessment and DRM decision-making. This includes the quantification and mapping of natural hazard risks and their components (i.e. hazard, exposure, and vulnerability), as well as the forecasting of hazard and impacts prior to a disaster event, or as it is unfolding (in real- or near real-time). In this context, the following thematic areas are of particular interest: artificial intelligence and machine learning, big data, remote sensing, social media, volunteered geographic information (VGI), mobile applications, crowdsourcing, internet of things (IoT), and blockchain. The special issue highlights how such innovations can support real-world DRM strategies and translate into improved DRM decisions.

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11 Aug 2021
The potential of machine learning for weather index insurance
Luigi Cesarini, Rui Figueiredo, Beatrice Monteleone, and Mario L. V. Martina
Nat. Hazards Earth Syst. Sci., 21, 2379–2405,,, 2021
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15 Jun 2021
Review article: Detection of actionable tweets in crisis events
Anna Kruspe, Jens Kersten, and Friederike Klan
Nat. Hazards Earth Syst. Sci., 21, 1825–1845,,, 2021
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06 May 2021
Opportunities and risks of disaster data from social media: a systematic review of incident information
Matti Wiegmann, Jens Kersten, Hansi Senaratne, Martin Potthast, Friederike Klan, and Benno Stein
Nat. Hazards Earth Syst. Sci., 21, 1431–1444,,, 2021
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16 Feb 2021
Are OpenStreetMap building data useful for flood vulnerability modelling?
Marco Cerri, Max Steinhausen, Heidi Kreibich, and Kai Schröter
Nat. Hazards Earth Syst. Sci., 21, 643–662,,, 2021
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21 Jan 2021
A glimpse into the future of exposure and vulnerabilities in cities? Modelling of residential location choice of urban population with random forest
Sebastian Scheuer, Dagmar Haase, Annegret Haase, Manuel Wolff, and Thilo Wellmann
Nat. Hazards Earth Syst. Sci., 21, 203–217,,, 2021
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10 Sep 2020
A spaceborne SAR-based procedure to support the detection of landslides
Giuseppe Esposito, Ivan Marchesini, Alessandro Cesare Mondini, Paola Reichenbach, Mauro Rossi, and Simone Sterlacchini
Nat. Hazards Earth Syst. Sci., 20, 2379–2395,,, 2020
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20 Aug 2020
Testing the impact of direct and indirect flood warnings on population behaviour using an agent-based model
Thomas O'Shea, Paul Bates, and Jeffrey Neal
Nat. Hazards Earth Syst. Sci., 20, 2281–2305,,, 2020
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20 May 2020
A multi-hazard risk prioritisation framework for cultural heritage assets
Giacomo Sevieri, Carmine Galasso, Dina D'Ayala, Richard De Jesus, Andres Oreta, Mary Earl Daryl A. Grio, and Rhodella Ibabao
Nat. Hazards Earth Syst. Sci., 20, 1391–1414,,, 2020
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