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.