Analysis and Prediction of Temporal Discharge Variations in Stormwater Drainage Systems Under Artificial and Natural Rainfall Conditions using Machine Learning

This study investigates the temporal variations in discharge from stormwater drainage wells through field monitoring under both artificial and natural rainfall conditions. By analyzing discharge patterns observed in controlled experiments and real-world rainfall events, the research identifies critical factors influencing the performance of stormwater drainage systems. A machine learning approach is employed to develop a predictive model, leveraging both experimental and field data to forecast discharge behavior with high accuracy. The model is validated through comprehensive comparisons with observed data to ensure robustness and practical applicability. The findings contribute to advancing the design, operation, and management of stormwater drainage systems, enhancing urban flood resilience and sustainable water management practices.