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

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.

Assessing the susceptibility of schools to flood events in Iran

“…Using AHP, the normalized rate (NR) of the five distance classes were determined (Table 3 ). The exposure map was prepared according to experts’ ratings for different school-vulnerability classes (based on distance from flood hazard zone) 15 , 88 – 90 and the AHP results (Fig. 7 ).…”

“…The sample was randomly divided into a modeling set containing 70 percent of the locations and a validation set containing 30% of the sample. As flood occurrence is determined by an interaction of natural and human processes, based on previous studies 15 , 53 – 55 12 of the most important effective factors were identified for use in modeling as input variables. They included elevation, slope, aspect, plan curvature, lithology, drainage density, annual rainfall, topographic wetness index (TWI), normalized difference vegetation index (NDVI), land use type, distance from nearest river, and distance from nearest road.…”

“…The susceptibility of schools to floods. Based on the natural break algorithm, the flood exposure map was classified into five classes (very low, low, moderate, high and very high) in ArcGIS 10.4.2 15,87 . To generate the final school flood-exposure map, the most accurate flood risk map and school exposure map were fed into the susceptibility equation: Flood susceptibility = Flood risk × School exposure.…”

“…Schools are settings that concentrate children and need special attention with regard to extreme natural events. Over the last few decades, the frequency of floods has been increasing and loss of life and property has accordingly increased 13 – 15 . So, it is important to assess susceptibility of schools to flood events to reduce damages and prevent loss of lives.…”

A New Hybrid Firefly–PSO Optimized Random Subspace Tree Intelligence for Torrential Rainfall-Induced Flash Flood Susceptible Mapping

“…Noticeably, very high precipitation intensity events often occur in the rainy season within a short period, observed in steep slopes, leading to the frequent occurrence of flash floods along with landslides in the case study. [27,31,32]. To prepare the flood-prone map, the initial step is to acquire the relevant data and to construct a spatial geodatabase.…”