GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility

A Case Study at Da Lat City, Vietnam

Document identifier: oai:DiVA.org:ltu-77157
Access full text here:10.3390/su11247118
Keyword: Engineering and Technology, Civil Engineering, Geotechnical Engineering, Teknik och teknologier, Samhällsbyggnadsteknik, Geoteknik, Landslides, Alternating decision trees, Bagging, Dagging, MultiBoostAB, RealAdaBoost, Hybrid models, Soil Mechanics
Publication year: 2019
Relevant Sustainable Development Goals (SDGs):

The SDG label(s) above have been assigned by OSDG.ai

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.

Authors

Viet-Tien Nguyen

Institute of Geological Sciences, Vietnam Academy of Science and Technology, Dong da, Hanoi, Vietnam. Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Cau Giay, Hanoi, Vietnam
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Trong Hien Tran

Institute of Geological Sciences, Vietnam Academy of Science and Technology, Dong da, Hanoi, Vietnam
Other publications >>

Ngoc Anh Ha

Institute of Geological Sciences, Vietnam Academy of Science and Technology, Dong da, Hanoi, Vietnam
Other publications >>

Van Liem Ngo

Faculty of Geography, VNU University of Science, Vietnam National University, Hanoi, Thanh Xuan, Hanoi, Vietnam
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Nadhir Al-Ansari

Luleå tekniska universitet; Geoteknologi
Other publications >>

Van Phong Tran

Institute of Geological Sciences, Vietnam Academy of Science and Technology, Dong da, Hanoi, Vietnam
Other publications >>

Huu Duy Nguyen

Faculty of Geography, VNU University of Science, Vietnam National University, Hanoi, Thanh Xuan, Hanoi, Vietnam
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M. A. Malek

Institute of Sustainable Energy, University Tenaga Nasional, Selangor, Malaysia
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Ata Amini

Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran
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Indra Prakash

Department of Science and Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar, India
Other publications >>

Lanh Si Ho

Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
Other publications >>

Binh Thai Pham

University of Transport Technology, Hanoi, Vietnam
Other publications >>

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        identifier: oai:DiVA.org:ltu-77157
        datestamp: 2021-04-19T12:40:57Z
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                recordCreationDate: 2019-12-12
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                http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-77157
                10.3390/su11247118
            titleInfo: 
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                    lang: eng
                title: GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility
                subTitle: A Case Study at Da Lat City Vietnam
            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.

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                        lang: eng
                        authority: uka.se
                    topic: 
                        Engineering and Technology
                        Civil Engineering
                        Geotechnical Engineering
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                        lang: swe
                        authority: uka.se
                    topic: 
                        Teknik och teknologier
                        Samhällsbyggnadsteknik
                        Geoteknik
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                        lang: eng
                    topic: landslides
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                    topic: alternating decision trees
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                    topic: bagging
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                    topic: dagging
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                    topic: MultiBoostAB
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                    topic: RealAdaBoost
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                    topic: hybrid models
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                    topic: Geoteknik
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                    topic: Soil Mechanics
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                Published
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Validerad;2020;Nivå 2;2019-12-16 (johcin)

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