مقایسه کارایی برخی از مدل های یادگیری ماشین در تهیه نقشه حساسیت پذیری به حرکات توده ای (مطالعه موردی: استان چهارمحال و بختیاری)

Mass movements are among the most dangerous natural hazards in mountainous regions. The present study employs machine learning (ML) models for mass movement susceptibility mapping (MMSM) in Iran based on a comprehensive dataset of ۸۶۴ mass movements which include debris flow, landslide, and rockfall during the last ۴۲ years (۱۹۷۷–۲۰۱۹) as well as ۱۲ conditional factors. The results of validation stage show that RF (random forest) is the most viable model for mass movement susceptibility maps. In addition, MARS (multivariate adaptive regression splines), MDA (mixture discriminant additive), and BRT (boosted regression trees) models also provide relatively accurate results. Results of the AUC for validation of produced maps were ۰.۹۶۸, ۰.۸۴۵, ۰.۸۲۸, and ۰.۷۶۵ for RF, MARS, MDA, and BRT, respectively. Based on MMSM generated by RF model, ۳۲% of study area is identified to be under high and very high susceptibility classes. Most of the endangered areas for mass movement are in the west and central parts of the Chaharmahal and Bakhtiari Province. In addition, our findings indicate that elevation, slope angle, distance from roads, and distance from faults are critical factors for mass movement. Our results provide a perspective view for decision makers to mitigate natural hazards.

کلیدواژه ها:

زمین لغزش ، هوش مصنوعی ، پهنه بندی ، جنگل تصادفی