Accurate prediction of daily inflows into reservoirs is crucial for effective water resource management and planning. This project introduces an artificial intelligence framework designed specifically for predicting daily inflows in the Drina River basin. It integrates machine learning algorithms with hydrological data, including historical inflow patterns, meteorological variables, and reservoir levels. The framework employs a combination of time series analysis, regression techniques, and neural networks to forecast inflows with high precision. Key components include data preprocessing to handle missing values in the observed data, feature engineering to extract relevant patterns, and model selection based on comparative performance metrics. The proposed approach is validated using real-world data from multiple reservoirs within the Drina River basin, demonstrating its capability to adapt to varying hydrological conditions and improve forecasting accuracy compared to traditional methods.<\/p>\n","protected":false},"featured_media":0,"parent":0,"template":"","project_category":[22],"class_list":["post-2692","project","type-project","status-publish","hentry","project_category-ongoing_projects"],"acf":[],"yoast_head":"\n