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.
Highlights
Landslides are one of the most devastating natural disasters all over the world, especially in mountain regions affecting the economy and lives of the people [1,2]
Vietnam is located in the tropical monsoon region, which is mostly affected by natural disasters like flash floods or landslides [6,7]
Four hybrid models, namely B-ADT, D-ADT, MBAB-ADT, RAB-ADT, which are a combination of alternating decision trees (ADT) and machine learning (ML) methods namely bagging, dagging, MultiBoostAB, and RealAdaBoost, respectively, were developed
Summary
Landslides are one of the most devastating natural disasters all over the world, especially in mountain regions affecting the economy and lives of the people [1,2]. With the development of computational power and geographic information system (GIS), nowadays, the quantitative approach—which is better and more objective than the qualitative approach—is more widely used in landslide studies This approach includes traditional statistical models and machine learning (ML) based models. The main objective is to develop and compare GIS based hybrid computational intelligence models to generate landslide susceptibility maps of the Da Lat province, which is one of Sustainability 2019, 11, 7118 the most landslide prone areas of Vietnam For this purpose, four hybrid models, namely B-ADT, D-ADT, MBAB-ADT, RAB-ADT, which are a combination of alternating decision trees (ADT) and ML methods namely bagging, dagging, MultiBoostAB, and RealAdaBoost, respectively, were developed. Weka software was used to construct and validate the models, whereas ArcGIS application was used to prepare datasets and generate maps
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