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• W3044200916 abstract "Abstract This study sought to produce an accurate multi-hazard risk map for a mountainous region of Iran. The study area is in southwestern Iran. The region has experienced numerous extreme natural events in recent decades. This study models the probabilities of snow avalanches, landslides, wildfires, land subsidence, and floods using machine learning models that include support vector machine (SVM), boosted regression tree (BRT), and generalized linear model (GLM). Climatic, topographic, geological, social, and morphological factors were the main input variables used. The data were obtained from several sources. The accuracies of GLM, SVM, and functional discriminant analysis (FDA) models indicate that SVM is the most accurate for predicting landslides, land subsidence, and flood hazards in the study area. GLM is the best algorithm for wildfire mapping, and FDA is the most accurate model for predicting snow avalanche risk. The values of AUC (area under curve) for all five hazards using the best models are greater than 0.8, demonstrating that the model’s predictive abilities are acceptable. A machine learning approach can prove to be very useful tool for hazard management and disaster mitigation, particularly for multi-hazard modeling. The predictive maps produce valuable baselines for risk management in the study area, providing evidence to manage future human interaction with hazards." @default.
• W3044200916 created "2020-07-29" @default.
• W3044200916 creator A5006705342 @default.
• W3044200916 creator A5031747582 @default.
• W3044200916 creator A5048549263 @default.
• W3044200916 creator A5062128630 @default.
• W3044200916 creator A5075958461 @default.
• W3044200916 creator A5091442115 @default.
• W3044200916 date "2020-07-22" @default.
• W3044200916 modified "2023-10-17" @default.
• W3044200916 title "A machine learning framework for multi-hazards modeling and mapping in a mountainous area" @default.
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