Exploring novel hybrid soft computing models for landslide susceptibility mapping in Son La hydropower reservoir basin
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...
Main Authors: | , , , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Taylor & Francis Group
2021-01-01
|
Series: | Geomatics, Natural Hazards & Risk |
Subjects: | |
Online Access: | http://dx.doi.org/10.1080/19475705.2021.1943544 |
id |
doaj-369160cc44444f8ca466e71136a924de |
---|---|
record_format |
Article |
spelling |
doaj-369160cc44444f8ca466e71136a924de2021-07-06T12:16:07ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132021-01-011211688171410.1080/19475705.2021.19435441943544Exploring novel hybrid soft computing models for landslide susceptibility mapping in Son La hydropower reservoir basinNguyen Van Dung0Nguyen Hieu1Tran Van Phong2Mahdis Amiri3Romulus Costache4Nadhir Al-Ansari5Indra Prakash6Hiep Van Le7Hanh Bich Thi Nguyen8Binh Thai Pham9Institute of Geography, Vietnam Academy of Science and TechnologyVNU University of Science, Vietnam National UniversityInstitute of Geological Sciences, Academy of Science and Technology (VAST)Department of Watershed & Arid Zone Management, Gorgan University of Agricultural Sciences & Natural ResourcesDepartment of Civil Engineering, Transilvania University of BrașovDepartment of Civil, Environmental and Natural Resources Engineering, Lulea University of TechnologyDDG (R) Geological Survey of IndiaUniversity of Transport TechnologyUniversity of Transport TechnologyUniversity of Transport TechnologyIn 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.http://dx.doi.org/10.1080/19475705.2021.1943544landslide susceptibilitymachine learningroc curvegisvietnam |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Nguyen Van Dung Nguyen Hieu Tran Van Phong Mahdis Amiri Romulus Costache Nadhir Al-Ansari Indra Prakash Hiep Van Le Hanh Bich Thi Nguyen Binh Thai Pham |
spellingShingle |
Nguyen Van Dung Nguyen Hieu Tran Van Phong Mahdis Amiri Romulus Costache Nadhir Al-Ansari Indra Prakash Hiep Van Le Hanh Bich Thi Nguyen Binh Thai Pham Exploring novel hybrid soft computing models for landslide susceptibility mapping in Son La hydropower reservoir basin Geomatics, Natural Hazards & Risk landslide susceptibility machine learning roc curve gis vietnam |
author_facet |
Nguyen Van Dung Nguyen Hieu Tran Van Phong Mahdis Amiri Romulus Costache Nadhir Al-Ansari Indra Prakash Hiep Van Le Hanh Bich Thi Nguyen Binh Thai Pham |
author_sort |
Nguyen Van Dung |
title |
Exploring novel hybrid soft computing models for landslide susceptibility mapping in Son La hydropower reservoir basin |
title_short |
Exploring novel hybrid soft computing models for landslide susceptibility mapping in Son La hydropower reservoir basin |
title_full |
Exploring novel hybrid soft computing models for landslide susceptibility mapping in Son La hydropower reservoir basin |
title_fullStr |
Exploring novel hybrid soft computing models for landslide susceptibility mapping in Son La hydropower reservoir basin |
title_full_unstemmed |
Exploring novel hybrid soft computing models for landslide susceptibility mapping in Son La hydropower reservoir basin |
title_sort |
exploring novel hybrid soft computing models for landslide susceptibility mapping in son la hydropower reservoir basin |
publisher |
Taylor & Francis Group |
series |
Geomatics, Natural Hazards & Risk |
issn |
1947-5705 1947-5713 |
publishDate |
2021-01-01 |
description |
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. |
topic |
landslide susceptibility machine learning roc curve gis vietnam |
url |
http://dx.doi.org/10.1080/19475705.2021.1943544 |
work_keys_str_mv |
AT nguyenvandung exploringnovelhybridsoftcomputingmodelsforlandslidesusceptibilitymappinginsonlahydropowerreservoirbasin AT nguyenhieu exploringnovelhybridsoftcomputingmodelsforlandslidesusceptibilitymappinginsonlahydropowerreservoirbasin AT tranvanphong exploringnovelhybridsoftcomputingmodelsforlandslidesusceptibilitymappinginsonlahydropowerreservoirbasin AT mahdisamiri exploringnovelhybridsoftcomputingmodelsforlandslidesusceptibilitymappinginsonlahydropowerreservoirbasin AT romuluscostache exploringnovelhybridsoftcomputingmodelsforlandslidesusceptibilitymappinginsonlahydropowerreservoirbasin AT nadhiralansari exploringnovelhybridsoftcomputingmodelsforlandslidesusceptibilitymappinginsonlahydropowerreservoirbasin AT indraprakash exploringnovelhybridsoftcomputingmodelsforlandslidesusceptibilitymappinginsonlahydropowerreservoirbasin AT hiepvanle exploringnovelhybridsoftcomputingmodelsforlandslidesusceptibilitymappinginsonlahydropowerreservoirbasin AT hanhbichthinguyen exploringnovelhybridsoftcomputingmodelsforlandslidesusceptibilitymappinginsonlahydropowerreservoirbasin AT binhthaipham exploringnovelhybridsoftcomputingmodelsforlandslidesusceptibilitymappinginsonlahydropowerreservoirbasin |
_version_ |
1721317517639548928 |