Assessment of Landslide Prediction and Route Suggestion using Satellite Digital Image Processing with Machine Learning

Abstract

newlineThe revolutionary study is an innovative attempt to forecast the likelihood of landslides in hilly areas in the future by utilizing state-of-the-art technology, particularly Convolutional Neural Networks (CNN) and Polygon approaches. Through the integration of satellite images from Bhuvan, SAS Planet, and Google Earth with relevant rainfall data, the study creates an extensive Digital Elevation Model (DEM) that forms the basis for predictive modeling. By utilizing CNN, which is widely recognized for its expertise in pattern recognition, in conjunction with polygon techniques for exact geographical analysis, the research develops a highly advanced prediction model that can precisely anticipate landslide occurrences. Interestingly, the study addresses the intricacies of hilly landscapes and their vulnerability to landslides, emphasizing practical usefulness. The practical methodology guarantees that the results are easily translated into useful insights for communities and local governments, rather than just being theoretical. The research reduces threats to public health, the environment, and local economies by providing stakeholders with early and accurate warnings, which in turn empowers proactive mitigation steps. A major advancement in landslide prediction and hazard management has been made with the incorporation of cutting-edge technology and thorough data analysis. This has improved community resilience and allowed for sustainable development in landslide-prone regions.