چکیده مقاله
Accurate prediction of total sediment load can play a crucial role in water resources engineering for managing river systems and mitigating sediment related risks In this study, we introduce a new and efficient data driven fusion framework that employs the outputs from the semi empirical and physically based Yang 1973, 1979 sediment transport equations as inputs of two ensemble machine learning ML techniques, Random Forest RF and extreme gradient boosting XGBoost , to predict total sediment concentrations in the Nestos River, Greece, using 111 field measurements ML models fuse the results of Yang models to achieve more accurate estimations Both models performed well During testing, both models demonstrated strong predictive power and generalization capability, significantly outperforming Yang's models In the testing phase, XGBoost recorded an R² of 0 7669 and an RMSE of 0 2742 Random Forest also yielded an R² of 0 7515 and an RMSE of 0 2831 Our results indicate that the proposed cutting edge approach significantly improved the predictive accuracy of traditional Yang equations used in previous studies This hybrid approach provided higher accuracy despite its simplicity and low computational costs, which makes it beneficial for engineering and management purposes, especially in data scarce rivers
کلیدواژهها
نویسندگان
Mohammad Parvaneh
Graduate Student, Department of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran
Gholam Reza Rakhshandehroo
Professor, Department of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran
Nasser Talebbeydokhti
Professor, Department of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran
شیوه ارجاع
Parvaneh, Mohammad and Rakhshandehroo, Gholam Reza and Talebbeydokhti, Nasser,1404,Prediction of Total Sediment Load Using a Novel Data-Driven Fusion of Semi-Empirical and Physically-Based Models,14th International Congress on Civil Engineering,Tehran
ارائهشده در
مجموعه مقالات چهاردهمین کنگره بین المللی مهندسی عمران29 مهر 1404 · تهران