IoT-Based Flood Detection and Management Systems in Urban Areas
Keywords:
IoT-based systems, Flood detection, Urban areas, Real-time monitoring, Machine learning, Flood managementAbstract
Urban flooding poses considerable challenges due to its economic, social, and environmental repercussions, particularly in areas experiencing rapid urbanization. This paper reviews recent developments in IoT applications for flood detection and management. It underscores different IoT frameworks that are employed to gather and oversee data from sensors that track hydrological, geological, and meteorological metrics. Furthermore, the research investigates how Artificial Neural Networks (ANN) are integrated into smart flood prediction systems, which enhance the scalability and reliability of flood management initiatives by evaluating critical environmental variables. The fusion of IoT with cloud computing and data analytics services has bolstered data processing capabilities. Conversely, the integration of IoT with Synthetic Aperture Radar (SAR) data provides effective solutions for monitoring and early warning systems. By synthesizing current research and identifying emerging trends, this survey aspires to offer insights into the efficacy and efficiency of current flood management strategies and their implications for enhancing urban resilience.
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