Smart Dam Control: Embedded Systems and LSTM-Based Water Level Prediction

Monitoring and controlling water levels in dams in real-time is crucial for safety and stability. We present a low-cost and scalable prototype for dam water level monitoring and control, employing an ESP8266 microcontroller and ultrasonic sensor. Additionally, we integrate machine learning models, including logistic regression and LSTM networks, for advanced water level prediction. These models have an accuracy of 97.31%, which is good and will keep improving as the data gets collected. Real-time monitoring is done with the help of Blynk, which relays data and helps make decisions according to the safety thresholds for anomaly detection and proactive prediction. The standalone system operates at 2.5 V, which reduces the running cost too. Our simulated dam environment testing validates the system’s effectiveness, highlighting its potential to enhance dam safety, water resource management, and applications in irrigation, hydroelectric power, and water management domains.