Data-driven Flood Prediction for Bangladesh at a 2-km Spatial Resolution Using ConvLSTM

Flood prediction is a critical component of disaster preparedness, especially given the substantial economic and social consequences of flooding. As climate change intensifies the frequency and severity of extreme weather events, the need for accurate flood forecasting becomes even more urgent. This study focuses on Bangladesh, applying Convolutional Long-Short Term Memory (ConvLSTM) architectures to predict flooding at a spatial resolution of approximately 2 km, using a straightforward set of publicly available satellite images. The objective is to explore whether machine learning can simplify flood prediction while expanding the coverage of existing models. The model produces pixel-level flood probability maps, showing promising results in predicting large-scale flooding with Area Under the Curve (AUC) values above 0.9 and Structural Similarity Index (SSIM) scores exceeding 0.8. These outputs are both plausible and readily integrable into existing or new disaster response frameworks. As many regions remain underserved by flood prediction systems, this work lays the foundation for scalable machine learning applications that can help mitigate inequalities exacerbated by the increasing risk of flooding.