Identification of Flood Prone Areas in Sumatra Barat Province Using Remote Sensing Data and Geographic Information System Based on Machine Learning - Rakuasa, Heinrich (Journal of International Multidisciplinary Research, 2024)
This research aims to identify flood-prone areas in West Sumatra Province using remote sensing data and Machine Learning-based Geographic Information System. By utilizing variables such as distance from the river, Normalized Difference Water Index (NDWI), elevation, Topographic Position Index (TPI), Normalized Difference Vegetation Index (NDVI), and Rainfall Data, this research was conducted on the Google Earth Engine platform. This analysis method supports resource optimization, public education, increased environmental safety, and the development of new technologies and research. The results of the research are expected to assist in mitigation and adaptation to future flood disasters, as well as provide more accurate and comprehensive insights for local governments, disaster management agencies, and communities. With this approach, it is expected to reduce flood risk and increase community resilience to natural disasters.