A survey of challenges in reducing the impact of geological hazards associated with earthquakes in Iran

Hosseini, Kambod Amini; Ghayamghamian, Mohammad Reza

doi:10.1007/s11069-012-0123-7

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…However, only 0.33% of the watershed is residential area. In this mountainous watershed, there are markedly high frequencies and magnitudes of natural hazards, but the level of exposure and risk are also increasing due to socio-economic changes such as the increase of population [50,51]. Some rivers and watercourses in this semiarid region generate flashy stormflows that rapidly peak following brief and intense storms which cause flashy floods in the area.…”

Section: Study Areamentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Study Areamentioning

confidence: 99%