Dam Volume Prediction Model Development Using ML Algorithms

However, accurate predictive models are essential for their operation, especially when dealing with fluctuating environmental conditions and increased demand. Traditional hydrological models often struggle to capture the complexity of such systems. The advent of machine learning (ML) offers new opportunities to enhance predictive capabilities by utilizing large datasets and advanced algorithms (Maity et al., 202 4) . This work aims to develop a machine - learning model that predicts dam volume using features such as water area, physical dam attributes, and other characteristics, including full supply capacity. Multiple models were iteratively built to improve predictive accuracy and performance comparison, each incorporating additional features to refine the outputs . Accurately monitoring reservoir storage is challenging since in - situ data are often unavailable; therefore, remote sensing observations of water extent and height combined with data - driven models are i ncreasingly used for reservoir volume estimation ( Ghosh et al., 2014; Hou et al., 2021) . This study seeks to enhance the precision of dam volume estimates, providing a valuable tool for decision - makers in water management.