Multi-hazard exposure mapping using machine learning techniques: a case study from Iran

Omid Rahmati, Saleh Yousefi, Zahra Kalantari, Evelyn Uuemaa, Teimur Teimurian, Saskia Keesstra, Tien Dat Pham, Dieu Tien Bui*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)
4 Downloads (Pure)

Abstract

Mountainous areas are highly prone to a variety of nature-triggered disasters, which often cause disabling harm, death, destruction, and damage. In this work, an attempt was made to develop an accurate multi-hazard exposure map for a mountainous area (Asara watershed, Iran), based on state-of-the art machine learning techniques. Hazard modeling for avalanches, rockfalls, and floods was performed using three state-of-the-art models-support vector machine (SVM), boosted regression tree (BRT), and generalized additive model (GAM). Topo-hydrological and geo-environmental factors were used as predictors in the models. A flood dataset (n = 133 flood events) was applied, which had been prepared using Sentinel-1-based processing and ground-based information. In addition, snow avalanche (n = 58) and rockfall (n = 101) data sets were used. The data set of each hazard type was randomly divided to two groups: Training (70%) and validation (30%). Model performance was evaluated by the true skill score (TSS) and the area under receiver operating characteristic curve (AUC) criteria. Using an exposure map, the multi-hazard map was converted into a multi-hazard exposure map. According to both validation methods, the SVM model showed the highest accuracy for avalanches (AUC = 92.4%, TSS = 0.72) and rockfalls (AUC = 93.7%, TSS = 0.81), while BRT demonstrated the best performance for flood hazards (AUC = 94.2%, TSS = 0.80). Overall, multi-hazard exposure modeling revealed that valleys and areas close to the Chalous Road, one of the most important roads in Iran, were associated with high and very high levels of risk. The proposed multi-hazard exposure framework can be helpful in supporting decision making on mountain social-ecological systems facing multiple hazards.

Original languageEnglish
Article number1943
Pages (from-to)1-20
Number of pages20
JournalRemote Sensing
Volume11
Issue number16
DOIs
Publication statusPublished - 2 Aug 2019
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2019. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • natural disasters
  • Sentinel-1
  • hazard
  • artificial intelligence
  • Asara watershed

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