Spatial prediction of shallow landslide using Bat algorithm optimized machine learning approach: A case study in Lang Son Province, Vietnam. (October 2019)

• Record Type: Journal Article
• Authors: Tien Bui, Dieu; Hoang, Nhat-Duc; Nguyen, Hieu; Tran, Xuan-Linh
• Is Part Of: Advanced engineering informatics. Volume 42(2019)
• Publication Date: 2019-10
• Subjects: Shallow landslide -- Least Squares Support Vector Classification -- Bat algorithm -- GIS -- Susceptibility map; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2019.100978
• Languages: English
• ISSNs: 1474-0346

Abstract

Graphical abstract: Highlights: Proposed a hybrid method for landslide susceptibility mapping. LSSVC is employed for pattern recognition. Bat Algorithm is used to optimize the model performance. A GIS database in Lang Son province (Vietnam) is employed. The hybrid model has a good prediction performance (CAR = 90.44%). Abstract: This study develops a machine learning method that hybridizes the Least Squares Support Vector Classification (LSSVC) and Bat Algorithm (BA), named as BA-LSSVC, for spatial prediction of shallow landslide. To construct and verify the hybrid method, a Geographic Information System (GIS) database for the study area of Lang Son province (Vietnam) has been employed. LSSVC is used to separate data samples in the GIS database into two categories of non-landslide (negative class) and landslide (positive class). The BA metaheuristic is employed to assist the LSSVC model selection process by fine-tuning its hyper-parameters: the regularization coefficient and the kernel function parameter. Experimental results point out that the hybrid BA-LSSVC can help to achieve a desired prediction with an accuracy rate of more than 90%. The performance of BA-LSSVC is also better than those of benchmark methods, including the Convolutional Neural Network, Relevance Vector Machine, Artificial Neural Network, and Logistic Regression. Hence, the newly developed model is a capable tool to assist local authority in landslide hazard mitigation and management.