Exploring novel hybrid soft computing models for landslide susceptibility mapping in Son La hydropower reservoir basin
| العنوان: | Exploring novel hybrid soft computing models for landslide susceptibility mapping in Son La hydropower reservoir basin |
|---|---|
| المؤلفون: | Dung, Nguyen Van, Hieu, Nguyen, Phong, Tran Van, Amiri, Mahdis, Costache, Romulus, Al-Ansari, Nadhir, 1947, Prakash, Indra, Le, Hiep Van, Nguyen, Hanh Bich Thi, Pham, Binh Thai |
| المصدر: | Geomatics, Natural Hazards and Risk. 12(1):1688-1714 |
| مصطلحات موضوعية: | Landslide susceptibility, machine learning, ROC curve, GIS, Vietnam, Soil Mechanics, Geoteknik |
| الوصف: | In this study, two novel hybrid models namely Bagging-based Rough Set (BRS) and AdaBoost-based Rough Set (ABRS) were used to generate landslide susceptibility maps of Son La hydropower reservoir basin, Vietnam. In total, 186 past landslide events and twelve landslides affecting factors (slope degree, slope aspect, elevation, curvature, focal flow, river density, rainfall, aquifer, weathering crust, lithology, fault density and road density) were considered in the modeling study. The landslide data was split into training (70%) and testing (30%) for the model's development and validation. One R feature selection method was used to select and prioritize the landslide affecting factors based on their importance in model prediction. Performance of the hybrid developed models was evaluated and also compared with single rough set (RS) and support vector machine (SVM) models using various standard statistical measures including area under the curve (AUC)-receiver operating characteristics (ROC) curve. The results show that the developed hybrid model BRS (AUC = 0.845) is the most accurate model in comparison to other models (ABRS, SVM and RS) in predicting landslide susceptibility. Therefore, the BRS model can be used as an effective tool in the development of an accurate landslide susceptibility map of the hilly area. |
| وصف الملف: | electronic |
| URL الوصول: | https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-86384 https://doi.org/10.1080/19475705.2021.1943544 https://ltu.diva-portal.org/smash/get/diva2:1580727/FULLTEXT01.pdf |
| قاعدة البيانات: | SwePub |
PDF full text from Publisher | تدمد: | 19475705 19475713 |
|---|---|
| DOI: | 10.1080/19475705.2021.1943544 |