2025 Volume 13 Issue 2 Article ID: 24-16079
Flood modeling is essential for effective flood risk management strategies, but the reliability of datasets has long been a significant obstacle to the accuracy of flood models, especially in ungauged or poorly gauged river basins. Advances in machine learning technology have provided solutions to many of these problems, particularly through the use of machine learning algorithms that significantly enhance data accuracy and reliability. This study aims to project flood conditions under climate and land use changes by leveraging machine learning-filtered data. The Global Land Cover and Land Use (GLCLU) maps and the Forest And Building Removed Copernicus Digital Elevation Model (FABDEM) were utilized to increase the accuracy of future flood projection. The future climate condition was applied by employing six global climate models (GCMs) under the Couple Model Intercomparison Project 6 (CMIP6). The study was conducted in Upper Citarum River Basin (UCRB) and analyzed a 2-years return period of rainfall event to represent the yearly flooding. The results show that the change in land cover and climate could increase the runoff volume by 2-10% until 2100. It could extend the affected area around 16-46%. This finding emphasizes the impact of land use and climate changes on flood conditions in UCRB. This study successfully projected future flood risk by leveraging machine learning filtered data. It increases the opportunity of future flood risk projection for poorly gauged river basins.
