Integration of GIS and Machine Learning Techniques for Mapping the Landslide-Prone Areas in the State of Goa, India

Abstract

A landslide susceptibility map (LSM) assists in reducing the danger of landslides by locating the landslide-prone locations within the designated area. One of the locations that are prone to landslides in India's Western Ghats of which Goa is a part. This article presents the LSMs prepared for the state of Goa using four standard machine learning algorithms, namely Logistic Regression (LR ), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Random Forest (RF). In order to create LSMs, a 78-point landslide inventory, as well as 14 landslide conditioning factors, has been used, including slope, elevation, aspect, total curvature, plan curvature, profile curvature, yearly rainfall, Stream Power Index, Topographic Wetness Index, distance to road, depth to bedrock/soil depth, soil type, lithology, and land use land cover. The most pertinent features for the models' construction have been chosen using the Pearson correlation coefficient test and the Random Forest method. The presence of landslides is shown to be strongly influenced by the distance to road, slope of the terrain, and the annual rainfall. The LSMs generated were classified into five levels ranging from very low susceptibility level to very high susceptible. The prediction accuracy, precision, recall, F1-score, area under the ROC (AUC-ROC), and True Skill Statistics (TSS) have been used to analyse and compare the LSMs created using various methodologies. All of these algorithms perform pretty well, as evidenced by the overall accuracy scores of 81.90% for LR, 83.33% for SVM, 81.94% for KNN, and 86.11% for RF. SVM and RF are the better approaches for forecasting landslide vulnerability in the research area, according to TSS data. The maximum AUC-ROC of 86% was achieved by the RF algorithm. The results of performance metrics lead to the conclusion that the tree-based RF approach is most appropriate for producing LSM for the state of Goa. The results of this study indicate that more landslide-prone areas can be found in the Sattari, Dharbandora, Sanguem, and Canacona regions of Goa.