Data-Driven Modeling for Sustainable Urban Drainage Management

General Background: Sustainable water and sanitation management is crucial in urban environments due to rapid urbanization and climate change, which strain underground engineering networks. Specific Background: The increasing frequency of extreme precipitation events and rising water demand necessitate advanced hydraulic modeling to optimize drainage and sewer systems. Knowledge Gap: Current methodologies lack accuracy in simulating flow dynamics, sediment accumulation, and energy losses in complex drainage networks, largely due to infrequent and costly CCTV inspections. Aims: This study aims to enhance hydraulic modeling accuracy by integrating real-world measurements with numerical simulations to assess system performance, optimize drainage efficiency, and mitigate flooding risks. Results: The findings highlight that pipe roughness, sediment deposition, and biofilm accumulation significantly alter hydraulic conditions over time. Regular monitoring and adaptive drainage designs are essential for improving system resilience and reducing maintenance costs. Novelty: This research underscores the importance of multi-purpose collectors and introduces a data-driven approach to drainage management, addressing limitations in existing models. Implications: The study provides critical insights for urban planners and engineers, advocating for smart sensor integration and predictive analytics to enable real-time monitoring and sustainable infrastructure development. Future research should incorporate climate projections and IoT-based monitoring for enhanced urban resilience. Highlights: Challenge: Urbanization and climate change strain drainage and sewer systems. Solution: Integrating real-world data with simulations improves hydraulic modeling accuracy. Impact: Smart sensors and predictive analytics enhance drainage efficiency and resilience. Keywords. Precipitation, sewerage, urbanization, cable network, pipeline, drains.