Landslide Susceptibility Level Mapping in Kozhikode, Kerala, Using Machine Learning-Based Random Forest, Remote Sensing, and GIS Techniques

Pradeep Kumar Badapalli, Anusha Boya Nakkala, Raghu Babu Kottala, Sakram Gugulothu (), Fahdah Falah Ben Hasher, Varun Narayan Mishra and Mohamed Zhran ()
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Pradeep Kumar Badapalli: CSIR-National Geophysical Research Institute, Hyderabad 500007, Telangana, India
Anusha Boya Nakkala: Department of Geology, Yogi Vemana University, Kadapa 516005, Andhra Pradesh, India
Raghu Babu Kottala: Department of Geology, Yogi Vemana University, Kadapa 516005, Andhra Pradesh, India
Sakram Gugulothu: CSIR-National Geophysical Research Institute, Hyderabad 500007, Telangana, India
Fahdah Falah Ben Hasher: Department of Geography and Environmental Sustainability, College of Humanities and Social Sciences, Princess Nourah Bint Abdulrahman University, P.O. BOX 84428, Riyadh 11671, Saudi Arabia
Varun Narayan Mishra: Amity Institute of Geoinformatics & Remote Sensing (AIGIRS), Amity University, Sector-125, Noida 201313, Uttar Pradesh, India
Mohamed Zhran: Public Works Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt

Land, 2025, vol. 14, issue 7, 1-25

Abstract: Landslides are among the most destructive natural hazards in the Western Ghats region of Kerala, driven by complex interactions between geological, hydrological, and anthropogenic factors. This study aims to generate a high-resolution Landslide Susceptibility Level Map (LSLM) using a machine learning (ML)-based Random Forest (RF) model integrated with Geographic Information Systems (GIS). A total of 231 historical landslide locations obtained from the Bhukosh portal were used as reference data. Eight predictive factors—Stream Order, Drainage Density, Slope, Aspect, Geology, Land Use/Land Cover (LULC), Normalized Difference Vegetation Index (NDVI), and Moisture Stress Index (MSI)—were derived from remote sensing and ancillary datasets, preprocessed, and reclassified for model input. The RF model was trained and validated using a 50:50 split of landslide and non-landslide points, with variable importance values derived to weight each predictive factor of the raster layer in ArcGIS. The resulting Landslide Susceptibility Index (LSI) was reclassified into five susceptibility zones: Very Low, Low, Moderate, High, and Very High. Results indicate that approximately 17.82% of the study area falls under high to very high susceptibility, predominantly in the steep, weathered, and high rainfall zones of the Western Ghats. Validation using Area Under the Curve–Receiver Operating Characteristic (AUC-ROC) analysis yielded an accuracy of 0.890, demonstrating excellent model performance. The output LSM provides valuable spatial insights for planners, disaster managers, and policymakers, enabling targeted mitigation strategies and sustainable land-use planning in landslide-prone regions.

Keywords: landslide susceptibility; machine learning (ML); random forest (RF); remote sensing and GIS (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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