ARTIFICIAL INTELLIGENCE TECHNIQUES FOR LAND SLIDE PREDICTION USING SATELLITE IMAGINORY

Abstract

Landslides are one of the most destructive natural disasters, causing significant loss of life, infrastructure
damage, and environmental degradation worldwide. Accurate and timely prediction of landslides remains a
challenging task due to the complex interaction of geological, hydrological, and climatic factors. In recent
years, advancements in Artificial Intelligence (AI) and the availability of high-resolution satellite imagery
have opened new avenues for improving landslide prediction systems.
This study explores the application of AI techniques such as Machine Learning (ML) and Deep Learning
(DL) for landslide prediction using satellite imagery. Various models, including Convolutional Neural
Networks (CNNs), Random Forest, and Support Vector Machines (SVM), are employed to analyze spatial
and temporal patterns in satellite data. These models integrate multiple influencing factors such as rainfall,
slope, soil type, vegetation cover, and land use.
The proposed approach aims to enhance prediction accuracy by leveraging feature extraction capabilities of
deep learning models combined with geospatial analysis. Experimental results demonstrate that AI-based
models outperform traditional statistical methods in identifying landslide-prone areas. This research
contributes to disaster management by providing an efficient and scalable solution for early warning systems
and risk assessment