Abstract: Dams are vital for water resource management, hydroelectric power generation, and flood control. However, traditional systems depend heavily on manual inspections and limited automation, which can delay critical responses to structural or environmental hazards. To address these challenges, this research proposes an Integrated Dam Automation System that combines IoT, image processing, and deep learning technologies for real-time monitoring and control. The system is built around an ESP32 microcontroller that gathers data from multiple sensors, including deep learning-based image analysis for crack detection, leakage sensors, pH and turbidity sensors for water quality assessment, and IoT-enabled sensors for water level monitoring and flood prediction. A dynamic, automated gate control mechanism is also included to regulate water levels effectively. With the addition of AI-powered predictive maintenance and remote monitoring through cloud integration, the system enhances the operational efficiency, responsiveness, and safety of dam infrastructure.
Keywords: Dam Automation, Deep Learning, Image Processing, Internet of Things, Real Time Monitoring.
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DOI:
10.17148/IJARCCE.2025.14630
