Development of a Predictive Model for Hydropower Capacity in Jiangsu Province Utilizing Machine Learning Techniques

The strategic importance and contradictions of hydropower development have become evident. As an economic powerhouse, Jiangsu Province's energy demand continues to grow, and hydropower, as a clean energy source, holds a significant position under the "dual carbon" goals. In recent years, machine learning has gradually matured in water resource management, but its potential in predicting hydropower capacity has yet to be fully realized. The variables affecting hydropower generation are investigated, and the seasonal fluctuations and long-term trends of hydropower capacity (such as the difference between wet and dry seasons) are analyzed based on the time series data from 2005 to 2024. This provides a basis for dynamically adjusting power generation strategies and fully exploring the potential of XGBoost in hydropower capacity prediction. A systematic approach using the XGBoost model is adopted to reduce the dimension of data and identify the crucial factors that impact water resources. XGBoost model is evaluated using a dataset on water resource management from Jiangsu Province. Accuracy metrics are used to gauge the performance of the XGBoost model. Prediction accuracy achieved by the XGBoost model's training is 83.3%, with further potential to optimize extended works. However, 100% accuracy was obtained model's testing. This approach facilitates a balanced water ecology sheet by integrating environmental and economic considerations. XGBoost model identifies total water resources. The systematic machine learning approach helps policymakers in deciding the appropriate way of managing water resources, and infrastructures. Sustainable resource management techniques support decision-making through data-driven insights.