GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam

Viet-Tien Nguyen, Trong Hien Tran, Ngoc Anh Ha, Ngo Van Liem, Al-Ansari Nadhir, Tran Van Phong, Huu Duy Nguyen, Malek M. A., Ata Amini, Indra Prakash, Lanh Si Ho and Binh Thai Pham
Additional contact information
Viet-Tien Nguyen: Institute of Geological Sciences, Vietnam Academy of Science and Technology, 84 Chua Lang, Dong da, Hanoi 100000, Vietnam
Trong Hien Tran: Institute of Geological Sciences, Vietnam Academy of Science and Technology, 84 Chua Lang, Dong da, Hanoi 100000, Vietnam
Ngoc Anh Ha: Institute of Geological Sciences, Vietnam Academy of Science and Technology, 84 Chua Lang, Dong da, Hanoi 100000, Vietnam
Ngo Van Liem: Faculty of Geography, VNU University of Science, Vietnam National University, Hanoi, 334 Nguyen Trai, Thanh Xuan, Hanoi 100000, Vietnam
Al-Ansari Nadhir: Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 971 87 Lulea, Sweden
Tran Van Phong: Institute of Geological Sciences, Vietnam Academy of Science and Technology, 84 Chua Lang, Dong da, Hanoi 100000, Vietnam
Huu Duy Nguyen: Faculty of Geography, VNU University of Science, Vietnam National University, Hanoi, 334 Nguyen Trai, Thanh Xuan, Hanoi 100000, Vietnam
Malek M. A.: Institute of Sustainable Energy, University Tenaga Nasional, Selangor 43000, Malaysia
Ata Amini: Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj 66177-15175, Iran
Indra Prakash: Department of Science and Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar 382007, India
Lanh Si Ho: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Binh Thai Pham: University of Transport Technology, Hanoi 100000, Vietnam

Sustainability, 2019, vol. 11, issue 24, 1-24

Abstract: Landslides affect properties and the lives of a large number of people in many hilly parts of Vietnam and in the world. Damages caused by landslides can be reduced by understanding distribution, nature, mechanisms and causes of landslides with the help of model studies for better planning and risk management of the area. Development of landslide susceptibility maps is one of the main steps in landslide management. In this study, the main objective is to develop GIS based hybrid computational intelligence models to generate landslide susceptibility maps of the Da Lat province, which is one of the landslide prone regions of Vietnam. Novel hybrid models of alternating decision trees (ADT) with various ensemble methods, namely bagging, dagging, MultiBoostAB, and RealAdaBoost, were developed namely B-ADT, D-ADT, MBAB-ADT, RAB-ADT, respectively. Data of 72 past landslide events was used in conjunction with 11 landslide conditioning factors (curvature, distance from geological boundaries, elevation, land use, Normalized Difference Vegetation Index (NDVI), relief amplitude, stream density, slope, lithology, weathering crust and soil) in the development and validation of the models. Area under the receiver operating characteristic (ROC) curve (AUC), and several statistical measures were applied to validate these models. Results indicated that performance of all the models was good (AUC value greater than 0.8) but B-ADT model performed the best (AUC= 0.856). Landslide susceptibility maps generated using the proposed models would be helpful to decision makers in the risk management for land use planning and infrastructure development.

Keywords: landslides; alternating decision trees; bagging; dagging; MultiBoostAB; RealAdaBoost; hybrid models (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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