Flood Model

Flood modeling aims to predict flood extent, depth, and timing to support disaster management and mitigation efforts. Current research heavily utilizes deep learning architectures, such as convolutional neural networks (CNNs), long short-term memory (LSTMs), and physics-informed neural networks (PINNs), often integrated with other techniques like attention mechanisms and ensemble methods, to improve accuracy and efficiency, especially for large-scale and extreme events. These advancements are crucial for enhancing flood forecasting accuracy, enabling near real-time warnings, and informing infrastructure planning and risk assessment, ultimately reducing societal and economic losses from flooding.