Multi-Hazard Exposure Mapping Using Machine Learning Techniques: A Case Study from Iran

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

doi:10.3390/rs11161943

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.

Highlights

This study investigates multi-hazard exposure using different machine learning approaches due to the following reasons: (1) Machine learning (ML) is a subfield of artificial intelligence where models can learn and improve themselves based on historical events; (2) ML models can identify trends and patterns in a large volume of data and involve continuous improvement during operation, which lets them make better decisions [48]; (3) they are capable of handling data that are multi-dimensional and multi-variety [30]; and (4) they can directly extract knowledge of natural disaster processes based on previous disaster occurrences and geo-environmental factors without human intervention, they do not need experts’
Hazard maps for the Asara watershed based on the boosted regression tree (BRT), generalized additive model (GAM), and support vector machine (SVM) models were created for avalanches (Figure 4), rockfalls (Figure 5), and floods (Figure 6)
A multi-hazard exposure study was conducted based on three natural disasters in a mountainous region in Iran

Summary

Introduction

Planners need hazard susceptibility maps to cope with natural disasters in mountainous areas, flash floods, avalanches, and rockfalls [8,25,28]. These three most common hazards can damage transportation systems, property, and danger human life in risk areas [28]. Even in areas with a wide range of natural risks, previous studies have mainly focused on one type of hazard [8,29,30,31,32]. A multi-risk assessment technique could be very useful for considering the interactive reactions of different hazards on surrounding areas [36,37]

Results

Discussion

Conclusion