Integrated Smart Dam Control System Using Machine Learning

Efficient management of dams and reservoirs is essential for optimizing water resources, preventing floods, and ensuring structural reliability of the dams. In Sri Lanka, manual dam operations introduce inefficiencies and potential risks. This study aims to develop an intelligent dam control system that integrates ML (Machine Learning) and IoT (Internet of Things) for dam gate automation, flood prevention, and dam crack analysis. The system automates sluice gate operations based on water demand predictions using LightGBM, enhances flood prevention through a pre-release strategy, and employs CNNs (Convolutional Neural Networks) for accurate crack detection in dam structures. Real-time data analytics, enabled by IoT sensors and cloud-based storage, enhance the system's scalability and reliability, allowing for more responsive and adaptive management. The results indicate significant improvements in water release scheduling, flood prevention accuracy, and crack analysis precision, demonstrating the practical viability of ML and IoT integration in dam safety. This data-driven, scalable approach provides a robust solution for optimized dam management, particularly suited to regions facing similar challenges where resources and oversight are limited.