Extreme Flooding in Pakistan: An AI-Powered Framework for Enhanced Urban Flood Management System

Muhammad Yasir Zaheen; Dure Jabeen; Ali Akbar Siddique; M. Aamir

Authors

Muhammad Yasir Zaheen Department of Computer Engineering., Sir Syed University of Engineering & Technology, Karachi, Pakistan
Dure Jabeen Faculty of Engineering, Sciences and Technology, Iqra University, Pakistan
Ali Akbar Siddique Faculty of Engineering, Sciences and Technology, Iqra University, Pakistan
M. Aamir Department of Telecommunication Engineering, Sir Syed University of Engineering, Karachi, Pakistan

Keywords:

Flood Prediction, Flash Flood Risk, Natural Disaster Pre-Planning, Flood Management System.

Abstract

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. The system employs the artificial intelligence model Support Vector Machine (SVM), in aggregation with ARIMA modeling, to attain a remarkable accuracy rate of 99.99% to forecast flood events. The model undergoes training with two decades of historical meteorological data to augment its predictive prowess and guarantee robust performance. The result shows that SVM performs with superior accuracy in comparison to other machine learning algorithms (MLAs) by effectively handling complex, multidimensional and multimodal 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 of real-time data obtained from Internet of Things (IoT) nodes combined with an advanced deep learning model to improve forecast accuracy, scalability and reduce response time, which will lead to minimizing damages.

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