Title: Landslide susceptibility assessment along the major transport corridor using decision tree model: a case study of Kullu-Rohtang Pass

Authors: Nirbhav; Anand Malik; Maheshwar; Mukesh Prasad

Addresses: Department of Geography, Delhi School of Economics, University of Delhi, Delhi, India ' Swami Shraddhanand College, University of Delhi, Delhi, India ' TGT Computer Science, Directorate of Education, Delhi Government, Delhi, India ' Faculty of Engineering and Information Technology, School of Computer Science, Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, Australia

Abstract: To lessen damages from landslides, the key challenge is to predict the events precisely and accurately. The objective of this study is to assess landslide susceptibility in the study area. To achieve this objective, a detailed landslide inventory has been prepared based on imagery data and frequent field visits of 153 rock slides and 44 debris slides. Nine landslide factors were prepared initially and their relationships with each other and with the type of landslide was analysed. Information gain ratio measure is used to eliminate triggering factors with least score. Train_test_split method was used to classify the dataset into training and testing groups. Decision tree classification model of machine learning was applied for landslide susceptibility model (LSM). The performance was evaluated using classification report and receiver operating characteristic (ROC) curve. Results obtained have proven that the decision tree classification model performed well with good accuracy in forecasting landslide susceptibility.

Keywords: landslide susceptibility modelling; LSM; machine learning; decision tree classification.

DOI: 10.1504/IJBIDM.2024.135172

International Journal of Business Intelligence and Data Mining, 2024 Vol.24 No.1, pp.1 - 24

Received: 31 Aug 2021
Accepted: 14 Apr 2022
Published online: 01 Dec 2023 *