Predictive Flood Prevention System: A Microarchitecture-Based Approach for Intelligent Dam Control

This paper aims to build a fully automatic control system that reduces the risk of flooding in reservoirs in advance. The developed solution combines the collected data on historical water levels, rainfall, and temperature, and calculates the estimated water level for the next fifteen days with a machine learning model based on LSTM. The obtained forecasts are converted into precise opening and closing of the gates of the dams with servo motors, thus preventing the water level from exceeding the safety limit in real time. The prototype was tested in a home environment on three plastic tanks and proved that it is possible to maintain a constant water level using pre-calculated opening angles. The system continues to operate safely based on internal rules even in the absence of a backup power line and an external weather forecast API. This approach provides a flexible and self-managing example that can be applied to Azerbaijan’s data-poor reservoirs. The project shows that when modern sensor technology, deep learning, and modular software work together, it is possible to significantly reduce flood risk, and manage water resources more efficiently.