Efficient Water Resource Management in Dam Basins using Machine Learning

Abstract

The purpose of this paper is to highlight the need for proper water management in dam basins in order to enhance effective water resources management and flood control in a changing climatic situation. This study uniquely combines meteorological data (temperature, precipitation) with hydrological indicators (reservoir levels, inflow, outflow) using a multi-model machine learning framework to improve integrated water resource management. Inputs such as meteorological data, including temperature and precipitation, combined with dam hydrology data like dam levels, inflows, and outflows, can assist in forecasting models. With machine learning, it is possible to detect trends in rainfall and inflows inflating water availability and adjust dam management in real-time, where necessary. This will improve flood management by providing a predictive tool for water-raising during rainfall, storage, and release in time to avert any spilling over. Also, hydropower and agricultural irrigation demand a systemic cross-platform approach to manage water resources. Anomaly detection models can inform operators of abnormal inflow trends and assist with quick water flow modulation. The proposed models demonstrated strong performance, with the KNN regressor achieving an R² score of 0.9443 and classification models like Logistic Regression attaining 99.5% accuracy. This study will help in enhancing decision-making in dam basin management by adding various datasets of water sources in the AI models to provide more acceptable ways of using water while minimizing risks associated with extreme weather and water production instability. The efficiency of the machine learning-based model has been measured in terms of R2 score, root means squared error (RMSE).