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  2. Vol 10, No 1 (2026)
  3. Mesioye

IOT AND ML-POWERED CYBER-PHYSICAL FRAMEWORK FOR REAL-TIME URBAN FLOOD RESILIENCE WITH GEOSPATIAL VISUALIZATION

Emmanuel Ayobami Mesioye(1), Johnson Bisi Oluwagbemi(2), Shade Racheal Akinbo(3), Mathew Oluwatosin Esan(4),


(1) McPherson University.
(2) McPherson University
(3) Lecturer II Federal University of Technology Akure
(4) Wesley University, Ondo city
Corresponding Author

Abstract


Urban flooding remains a disastrous challenge for rapidly expanding cities in developing nations. Despite the fact deep learning models and IoT sensing are individually established in hydrology, their seamless integration into a unified, cost-effective Cyber-Physical System (CPS) specifically architected for data-scarce and infrastructure challenged environments remains a critical research gap. This research contributes a novel, end-to-end framework that bridges this divide by harmonizing three distinct pillars: a low-cost, energy-autonomous IoT sensor network, a hybrid CNN-LSTM predictive model, and a dynamic geospatial visualization dashboard. Unlike conventional systems designed for data-rich environments, our framework is contextually adapted for the unique topographical and socio-technical realities of Nigerian urban centers. Validated through a six-month deployment in the high-density Ajeromi-Ifelodun region of Lagos, the system achieved a Nash-Sutcliffe Efficiency (NSE) of 0.89 and a critical 4.5-hour forecast lead time.

Keywords


Urban flooding, Cyber-physical systems; Internet of Things; Machine learning; ML-LSTM; Climate Adaptation; Global South

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DOI: 10.56327/ijiscs.v10i1.1877

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