Efficient Dam Water Level Management Using Cloud-Based Data Analytics and LSTM Networks

The proposed work in this paper integrates cloud-based data analytics with Long Short-Term Memory (LSTM) networks to improve dam water level control. Traditional dam water level monitoring systems sometimes fail to predict and manage water levels, posing problems like floods or inadequate water delivery. The dam water level prediction system uses cloud computing and LSTM networks to deliver real-time, accurate forecasts. Large datasets from dam sensors are processed and analyzed efficiently by the cloud-based data analytics platform. The complicated and dynamic dam water level changes are modeled using LSTM networks, which can capture long-term dependencies in time-series data. LSTM networks enhance water level predictions by learning from prior data. The cloud-based architecture integrates with dam management systems for remote monitoring and control. Dam operations become more efficient and responsive, allowing early interventions to avoid catastrophes or optimize water resource use. The system tackles conventional dam water level control issues and offers a scalable and customizable framework for integration into varied dam infrastructures. Experimental findings show that the suggested technique manages dam water levels reliably and efficiently.