AI-Enabled Big Data Models for Urban Flood Prediction and Management
DOI:
https://doi.org/10.15662/IJEETR.2024.0606019Keywords:
Urban Flooding, AI-Enabled Big Data Models, Urban Hydrology Analytics, Smart City Infrastructure, City-Scale Flood Prediction, Drainage Network Optimization, Real-Time Flood Detection and Tracking, Urban Data Landscape, IoT-Based Environmental Monitoring, Flood Risk Management Systems, Data Governance Frameworks, Privacy and Security in Smart Cities, Data Quality Assurance, Scalable AI Architectures, Digital Transformation in Urban Systems, Integrated Disaster Management Platforms, Predictive Hydrological Modeling, Transferable Urban Analytics Models, Citybrain Architecture, Resilient Urban EcosystemsAbstract
Urban flooding is a global disaster that claims thousands of lives every year, causing immense economic damage and distress. With digital transformation, large amounts of disparate data are generated and stored. However, the large volume and complexity of Big Data in urban hydrology have outpaced its utilization for practical applications. New AI-enabled big-data models allow data-rich and information-hungry problems to meet. A comprehensive, objective summary of such models and their use in urban flood prediction and management fills a gap in the literature. Evidence for the synthesis comes from recent theoretical and applied studies and covers new insights into urban flooding, the emerging Urban-Data Landscape, a generic methodological framework, and major developments at three levels: city-scale flood prediction, drainage-network optimization, and real-time flood detection and tracking.
Despite the pressing need for comprehensive Data Protection Frameworks that embrace equity, privacy, and security for IoT and Smart Cities, the focus in AI-assisted urban flood management has been exclusively on prediction and optimization – with little effort to define the necessary governance frameworks for operational deployment. The importance of a holistic and integrated approach that preserves the integrity of urban society is highlighted, attention is drawn to data gaps and quality-assurance challenges, and the transferability and scalability of AI-enabled Big-Data Models are examined – towards the development of Citybrain, mind and soul of the Smart City.
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