Urban Flood

Urban flooding, a growing concern exacerbated by climate change and urbanization, is the focus of intense research aimed at improving prediction, detection, and mitigation strategies. Current research emphasizes the development and application of advanced machine learning models, including deep learning architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs), often combined with graph-based methods to capture spatial dependencies and integrate diverse data sources (e.g., satellite imagery, sensor data, social media). These efforts aim to enhance the accuracy and speed of flood prediction, enabling more effective real-time response and improved risk assessment, ultimately leading to better urban planning and disaster management.