AI-Driven Smart Dam Management System for Enhanced Safety and Flood Prevention.

Water is essential to everyday life and plays a vital role across various sectors. With the growing challenges related to water management, innovative approaches such as adaptive strategies, remote sensing technologies, and integrated water security frameworks are being developed. Dam safety has become increasingly important due to the ageing of infrastructure, seismic risks, and the intensifying impacts of extreme weather events. As a result, it has emerged as a key focus area within national disaster management plans. Governments are introducing regulatory frameworks, and institutions are adopting technical and structural safeguards to ensure dam integrity. However, a major concern remains--the absence of standardised emergency water release protocols. To address this, the project proposes an AI-powered dam monitoring and control system utilising a Stacked Dense LSTM (Long Short-Term Memory) model. This system analyses real-time data on water levels, temperature, humidity, and rainfall, which are stored in the cloud. The LSTM model predicts potential threats and manages water discharge through automated gate control, reducing the risk of dam overflow and flooding. Sudden floods caused by rising rivers, heavy rainfall, or lake overflow can lead to widespread destruction, displacing families, damaging agriculture, and causing long-term disruptions. The proposed predictive system enables proactive dam management, significantly reducing flood risks and enhancing the resilience of vulnerable communities. Major Findings: The AI-driven Smart Dam Management System proposed in the study effectively addresses critical challenges in flood prevention and dam safety by utilising a Stacked Dense LSTM model for real-time water level prediction and automated gate control. Through the integration of environmental sensors, cloud-based data storage, and intelligent analytics, the system enhances forecasting accuracy, mitigates overflow risks, and ensures timely interventions. The findings highlight its transformative potential in improving community resilience, supporting sustainable water resource management, and bridging existing gaps in emergency response protocols. [ABSTRACT FROM AUTHOR]