Predicting Reservoir Resource Utilization Using Machine Learning

The key issues in Reservoir Usage are the Computation of Reservoir Storage and Prediction of Water Levels using Machine Learning. The research integrates complex underground water techniques with AI to forecast water level trends in related reservoirs. and the application of Artificial Intelligence (AI) and Machine Learning (ML) algorithms, including Bayesian Networks (BN), Decision Trees (DT), k-nearest Neighbors (kNN), and Support Vector Machines (SVM), Artificial Neural Networks (ANN), Rule-Based optimization, Long Short-Term Memory networks (LSTM) for handling heterogeneous data to provide solutions. Quantifying reservoir operations is an essential process for the efficient management of water resources. Water level records are often the only data available for reservoir operation analysis, which causes an ill-posed problem in water resource system planning. This study proposes a framework to estimate reservoir operations using machine learning models and reasoning carefully derived from expert knowledge and soft data.