Flood Risk Modelling Based on Machine Learning Using Google Earth Engine in Hulu Sungai Utara Regency

Abstract

Abstract: This study emphasizes the importance of Google Earth Engine as a critical instrument for assessing the susceptibility of Hulu Sungai Utara Province to flooding. By integrating high-quality satellite data, detailed urban surface mapping, and sophisticated geospatial analysis capabilities, the technology has facilitated a more comprehensive comprehension of the factors that contribute to flood risk in this densely populated region. The study employs variables including the Normalized Difference Vegetation Index (NDVI), elevation, distance from rivers, and the Topographic Position Index (TPI). The data processing and analysis were conducted using Google Earth Engine.  The findings revealed that 47,875.86 hectares (51.66%) of the area fall into the low flood risk category, 39,763.08 hectares (42.90%) are classified as moderate risk, and 5,040.36 hectares (5.44%) are considered high-risk areas. The findings of this investigation have the potential to assist the Hulu Sungai Utara Provincial Government in the implementation of flood mitigation strategies.