Urban flooding poses considerable challenges for metropolitan areas, contributing to rapid urbanization and significant climatic change. This research develops a machine learning-based Urban Flood Management System (UFMS) to predict and manage flood risks, incorporating an enhanced risk warning system for rapidly urbanizing areas. The mitigation of urban flooding parameters, such as rainfall intensity, humidity, temperature, soil moisture, land use, and drainage network capacity, is analyzed in the UFMS. Thesystem employs the artificial intelligence model Support Vector Machine (SVM), in conjunction with ARIMA modeling, achieving a high accuracy rate of 99.99% in flood prediction to forecast flood events. The model undergoes training with two decades of historical meteorological data to augment its predictive prowess and guarantee robust performance. Results show that SVM outperforms other machine learning algorithms in handling complex, multidimensional flood data. This hybrid methodology provides real-time and highly accurate prediction of upcoming floods that leads to actionable insights for urban planners and emergency response teams. Future improvements may involve the utilization ofreal-time data obtainedfrom Internet of Things (IoT) nodes combined with an advanced deep learning model to improve forecast accuracy, scalability,and reduce response time, ultimately contributing to reduced flood-related damage.
Zaheen et al. (Fri,) studied this question.