Application of geographical information system (GIS) using artificial neural networks (ANN) for landslide study in Langat Basin, Selangor

Selamat, Siti Norsakinah; Majid, Nuriah Abd; Taha, Mohd Raihan; Osman, Ashraf

Abstract

The landslide was recognized as the most common geologic hazard around the world. The assessment of the relationship landslide conditioning factors is a critical step in managing landslide hazards and risks. Several models have been made to develop the landslide model in recent years. The Artificial Neural Networks (ANN) model was used in this study to develop a landslide model and to identify the most important landslide conditioning factors. Eight conditioning factors, including elevation, slope, aspect, curvature, lithology, soil series, Topographic Wetness Index (TWI), and rainfall, were selected and analyzed using the Geographical Information System (GIS) approach. The multilayer perceptron module and one hidden layer method extracted weighted conditioning factors. The landslide model was validated using the area under the curve (AUC) method. This model validation showed a success rate for training and testing is 0.876, respectively. This study found curvature is the most crucial factor affecting landslide occurrence in the Langat Basin with a 0.213 weight index, followed by rainfall (0.143) and elevation (0.141). Finally, the landslide model can be used as an indicator to identify the most important landslide conditioning factors and assess the relationship between these factors and landslide occurrences.

Citation

Selamat, S. N., Majid, N. A., Taha, M. R., & Osman, A. (2022). Application of geographical information system (GIS) using artificial neural networks (ANN) for landslide study in Langat Basin, Selangor. IOP Conference Series: Earth and Environmental Science, 1064(1), https://doi.org/10.1088/1755-1315/1064/1/012052

• Journal Article Type: Article
• Publication Date: 2022
• Deposit Date: Sep 28, 2022
• Publicly Available Date: Sep 29, 2022
• Journal: IOP Conference Series: Earth and Environmental Science
• Print ISSN: 1755-1307
• Electronic ISSN: 1755-1315
• Publisher: IOP Publishing
• Peer Reviewed: Peer Reviewed
• Volume: 1064
• Issue: 1
• DOI: https://doi.org/10.1088/1755-1315/1064/1/012052
• Public URL: https://durham-repository.worktribe.com/output/1190959