Revolutionizing urban flooding predictions: a segmented deep learning model with fine-tuning update capabilities

Urban flooding, intensified by climate change and urbanization, poses significant threats to public safety and economic growth, with annual costs exceeding $100 billion. This research addresses this pressing issue by introducing a transformative approach to urban flooding forecasting through advanced artificial intelligence. Central to our innovation is a segmented deep learning model that effectively captures the diverse characteristics of urban environments, facilitating more accurate flood predictions. Its fine-tuning capabilities enable the model to adapt to real-time data and dynamic urban conditions, resulting in significantly enhanced predictive performance. This advancement not only surpasses the accuracy of traditional forecasting methods but also offers substantial potential for real-time applications, supporting city planners and emergency responders in proactive flood management.