Landslide Susceptibility Model Using Artificial Neural Network (ANN) Approach in Langat River Basin, Selangor, Malaysia

Siti Norsakinah Selamat, Nuriah Abd Majid, Mohd Raihan Taha and Ashraf Osman
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Siti Norsakinah Selamat: Institute for Environment and Development (LESTARI), Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
Nuriah Abd Majid: Institute for Environment and Development (LESTARI), Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
Mohd Raihan Taha: Department of Civil Engineering, Faculty of Engineering and Build Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
Ashraf Osman: Department of Engineering, The Palatine Centre, Durham University, Stockton Road, Durham DH1 3LE, UK

Land, 2022, vol. 11, issue 6, 1-21

Abstract: Landslides are a natural hazard that can endanger human life and cause severe environmental damage. A landslide susceptibility map is essential for planning, managing, and preventing landslides occurrences to minimize losses. A variety of techniques are employed to map landslide susceptibility; however, their capability differs depending on the studies. The aim of the research is to produce a landslide susceptibility map for the Langat River Basin in Selangor, Malaysia, using an Artificial Neural Network (ANN). A landslide inventory map contained a total of 140 landslide locations which were randomly separated into training and testing with ratio 70:30. Nine landslide conditioning factors were selected as model input, including: elevation, slope, aspect, curvature, Topographic Wetness Index (TWI), distance to road, distance to river, lithology, and rainfall. The area under the curve (AUC) and several statistical measures of analyses (sensitivity, specificity, accuracy, positive predictive value, and negative predictive value) were used to validate the landslide predictive model. The ANN predictive model was considered and achieved very good results on validation assessment, with an AUC value of 0.940 for both training and testing datasets. This study found rainfall to be the most crucial factor affecting landslide occurrence in the Langat River Basin, with a 0.248 weight index, followed by distance to road (0.200) and elevation (0.136). The results showed that the most susceptible area is located in the north-east of the Langat River Basin. This map might be useful for development planning and management to prevent landslide occurrences in Langat River Basin.

Keywords: landslide; predictive model; correlation analysis; Artificial Neural Network; Langat River Basin (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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