A Dual-Model Spatiotemporal Flood Prediction System Using Sentinel-1 SAR Imagery and Meteorological Data: A Case Study in Palembang, Indonesia

Abstract

Floods remain one of the most destructive and frequent natural hazards, especially in urban river basins like Palembang, Indonesia. Improving early warning systems through the integration of radar and meteorological data has become increasingly feasible with advances in remote sensing and machine learning. The research contribution is the development of a dual-model spatiotemporal prediction framework that combines Sentinel-1 SAR imagery and meteorological data using eXtreme Gradient Boosting (XGBoost) for spatial classification and Long Short-Term Memory (LSTM) for temporal forecasting. Radar backscatter values were extracted from Sentinel-1 Single Look Complex (SLC) data and processed into one-kilometer resolution grids using SNAP and QGIS. These were merged with weather variables precipitation, humidity, wind speed, and solar radiation sourced from local meteorological stations. The XGBoost model achieved a precision of 98.3%, accuracy of 99.94%, recall of 97.5%, and F1-score of 97.9%, with SHAP analysis identifying rainfall and wind speed as dominant flood predictors. Spatial predictions aligned closely with historically flood-prone areas along the Musi River. In contrast, the LSTM model, despite forecasting floods up to 12 days in advance with average accuracy of 91.6%, suffered from class imbalance, resulting in a recall of only 22.9% and precision of 36.3%, limiting its applicability for real-time early warning. These findings demonstrate that while spatial classification using radar and weather data is highly effective, temporal forecasting remains challenged by data imbalance and uneven class distribution. Future research should explore cost-sensitive learning, uncertainty quantification, and real-time validation to enhance the system’s operational reliability