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Flood, landslides, forest fire, and earthquake susceptibility maps using machine learning techniques and their combination

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Abstract

Protection against natural hazards (i.e., floods, landslides, forest fires, and earthquakes) is vital in land-use planning, especially in high-risk areas. Multi-hazard susceptibility maps can be used by land-use manager to guide urban development, to minimize the risk of natural disasters. The objective of the present study was to use four machine learning models to produce multi-hazard susceptibility maps in Khuzestan Province, Iran. In this work, four different natural hazards (flood, landslides, forest fire, and earthquake) using support vector machine (SVM), boosted regression tree (BRT), random forest (RF), and maximum entropy (MaxEnt) techniques were created. Effective factors used in the study include elevation, slope degree, slope aspect, rainfall, temperature, lithology, land use, normalized difference vegetation index (NDVI), wind exposition index (WEI), topographic wetness index (TWI), plan curvature, drainage density, distance from roads, distance from rivers, and distance from villages. The spatial earthquake hazard in the study area was derived from a peak ground acceleration (PGA) susceptibility map. The second step in the study was to combine the model-generated maps of the four hazards in a reliable multi-hazard map. The mean decrease Gini (MDG) method was used to determine the level of importance of each effective factor on the occurrence of landslides, floods, and forest fires. Finally, “area under the curve” (AUC) values were calculated to validate the forest fire, flood, and landslide susceptibility maps and to compare the predictive capability of the machine learning models. The RF model yielded the highest AUC values for the forest fire, flood, and landslide susceptibility maps, specifically, 0.81, 0.85, and 0.94, respectively.

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Data availability

The datasets generated and/or analyzed during the current study are not publicly available due [This work is ongoing in other parts] but are available from the corresponding author on reasonable request.

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Funding

This study was supported by Iran National Science Foundation (INSF) (Grant No. 99011991). Thanks to INSF.

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Authors and Affiliations

  1. Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran

    Hamid Reza Pourghasemi & Soheila Pouyan

  2. Department of Remote Sensing and GIS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Mojgan Bordbar

  3. Department of Water Engineering and Oceanic and Atmospheric Research Center, College of Agriculture, Shiraz University, Shiraz, Iran

    Foroogh Golkar

  4. Department of Earth Sciences, Institute for Quaternary Research, Simon Fraser University, Burnaby, BC, Canada

    John J. Clague

  5. Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania “Luigi Vanvitelli”, Via Vivaldi 43, 81100, Caserta, Italy

    Mojgan Bordbar

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HRP, SP, MB, FG, and JJC designed the experiments, ran models, analyzed the results, and wrote and reviewed the manuscript.

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Correspondence to Hamid Reza Pourghasemi.

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Pourghasemi, H.R., Pouyan, S., Bordbar, M. et al. Flood, landslides, forest fire, and earthquake susceptibility maps using machine learning techniques and their combination. Nat Hazards 116, 3797–3816 (2023). https://doi.org/10.1007/s11069-023-05836-y

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