SMART FLOOD PREVENTION: A REVIEW OF CUTTING-EDGE DATA SCIENCE MODELS FOR EARLY URBAN FLOOD DETECTION AND RESPONSE SYSTEMS
DOI:
https://doi.org/10.71146/kjmr620Keywords:
Urban flooding, flood prediction, machine learning, deep learning, hydrological modeling, early warning systems, IoT, decision support systems, predictive analyticsAbstract
Urban flooding has become increasingly prevalent due to climate change, rapid urbanization, and aging infrastructure. This systematic review examines the current state-of-the-art in data science models for early prediction of urban flooding and the associated preventive measure software systems. Through a comprehensive analysis of 97 research papers published between 2015 and 2024, we categorize and evaluate various modeling approaches, data sources, prediction accuracy, and implementation challenges. Our findings reveal a significant shift toward hybrid modeling approaches that combine physical and data-driven methods, an increased integration of IoT sensor networks, and a growing adoption of machine learning and deep learning techniques. We also identify gaps in current research, including limited real-time data processing capabilities, challenges in model scalability, and the need for improved uncertainty quantification [1]. This review provides a foundation for researchers and practitioners working on flood prediction systems and highlights promising directions for future research.
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Copyright (c) 2025 Muhammad Adeel Mannan, Sadiq Ur Rehman, Saad Akbar, Mohammad Ayub Latif (Author)
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