Advances in machine learning for natural hazards risk assessment
Advances in machine learning for natural hazards risk assessment
Editor(s): Vitor Silva, Caroline M. Gevaert, David Lallemant, Sabine Loos, and Philip Ward
The rise in the global population, the effects of climate change, and the growing urbanization in regions prone to natural hazards are some of the factors contributing to the exponential increase in the economic and human losses due to natural hazards. In response to this global challenge, several international agendas such as the Sendai Framework for Disaster Risk Reduction, the United Nations 17 Sustainable Development Goals, and the Paris Climate Agreement specifically demand a better understanding of disaster risk and impose specific risk reduction targets for the upcoming decades. Yet understanding, quantifying, and monitoring risk remain a challenge, particularly in areas where traditional risk data are limited. In the last decade, scientists, risk modellers, and data analysts have explored cutting-edge technologies such as artificial intelligence and data sources with unprecedented detail and spatial resolution to improve the reliability, accuracy, and geographical coverage of disaster risk assessment studies. The huge rise in popularity of these methods also comes with potential risks. This special issue fills an urgent need to properly understand the potential for AI and machine learning in the field of disaster risk analysis, communicate potential pitfalls and limitations, and provide examples of best practice. This special issue presents some of the recent advances and applications of machine learning in natural hazards risk assessment, covering the following topics:
  • exposure data collection and automatic building classification;
  • risk mapping and damage assessment;
  • development of hazard intensity and vulnerability models;
  • monitoring population and urban growth;
  • post-disaster damage and loss detection;
  • modelling of socio-economic vulnerability;
  • when to use AI or machine learning vs. when to use conventional disaster risk analysis models;
  • data, models, uncertainty, and performance metrics;
  • ethical considerations in the use of AI for disaster risk modelling.

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01 Dec 2023
Machine-learning-based nowcasting of the Vögelsberg deep-seated landslide: why predicting slow deformation is not so easy
Adriaan L. van Natijne, Thom A. Bogaard, Thomas Zieher, Jan Pfeiffer, and Roderik C. Lindenbergh
Nat. Hazards Earth Syst. Sci., 23, 3723–3745,,, 2023
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15 Jun 2023
Using machine learning algorithms to identify predictors of social vulnerability in the event of a hazard: Istanbul case study
Oya Kalaycıoğlu, Serhat Emre Akhanlı, Emin Yahya Menteşe, Mehmet Kalaycıoğlu, and Sibel Kalaycıoğlu
Nat. Hazards Earth Syst. Sci., 23, 2133–2156,,, 2023
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12 May 2023
Reduced-order digital twin and latent data assimilation for global wildfire prediction
Caili Zhong, Sibo Cheng, Matthew Kasoar, and Rossella Arcucci
Nat. Hazards Earth Syst. Sci., 23, 1755–1768,,, 2023
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03 May 2023
Probabilistic and machine learning methods for uncertainty quantification in power outage prediction due to extreme events
Prateek Arora and Luis Ceferino
Nat. Hazards Earth Syst. Sci., 23, 1665–1683,,, 2023
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22 Mar 2023
Development of a seismic loss prediction model for residential buildings using machine learning – Ōtautahi / Christchurch, New Zealand
Samuel Roeslin, Quincy Ma, Pavan Chigullapally, Joerg Wicker, and Liam Wotherspoon
Nat. Hazards Earth Syst. Sci., 23, 1207–1226,,, 2023
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01 Dec 2022
Comparison of machine learning techniques for reservoir outflow forecasting
Orlando García-Feal, José González-Cao, Diego Fernández-Nóvoa, Gonzalo Astray Dopazo, and Moncho Gómez-Gesteira
Nat. Hazards Earth Syst. Sci., 22, 3859–3874,,, 2022
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22 Nov 2022
Landsifier v1.0: a Python library to estimate likely triggers of mapped landslides
Kamal Rana, Nishant Malik, and Ugur Ozturk
Nat. Hazards Earth Syst. Sci., 22, 3751–3764,,, 2022
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24 Oct 2022
What weather variables are important for wet and slab avalanches under a changing climate in a low-altitude mountain range in Czechia?
Markéta Součková, Roman Juras, Kryštof Dytrt, Vojtěch Moravec, Johanna Ruth Blöcher, and Martin Hanel
Nat. Hazards Earth Syst. Sci., 22, 3501–3525,,, 2022
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16 Sep 2022
Machine learning models to predict myocardial infarctions from past climatic and environmental conditions
Lennart Marien, Mahyar Valizadeh, Wolfgang zu Castell, Christine Nam, Diana Rechid, Alexandra Schneider, Christine Meisinger, Jakob Linseisen, Kathrin Wolf, and Laurens M. Bouwer
Nat. Hazards Earth Syst. Sci., 22, 3015–3039,,, 2022
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