PREDICTIVE MODELING OF LANDSLIDE HAZARDS USING GIS AND ARTIFICIAL INTELLIGENCE
Abstract
Landslides are among the most destructive natural hazards globally, causing loss of life, infrastructure damage, and environmental degradation. The advent of Geographic Information Systems (GIS) and Artificial Intelligence (AI) has revolutionized predictive modeling of landslide susceptibility. This research develops a comprehensive predictive model integrating GIS spatial analysis and AI algorithms—including Random Forest (RF), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN)—to forecast landslide-prone areas with high accuracy. Using a combination of topographic, lithological, hydrological, climatic, and land-use datasets from selected high-risk regions in Uzbekistan, the model identifies critical factors contributing to slope instability. Validation against historical landslide occurrences and comparison with international datasets, such as USGS and Copernicus global landslide models, demonstrates significant improvement in predictive reliability. The study also identifies challenges, including data scarcity, spatial and temporal resolution limitations, and computational constraints, while proposing practical mitigation strategies. These findings offer a replicable framework for hazard management and risk reduction in Central Asia and similar geotectonic regions.
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