Modeling the damming effect on hydrological alteration and
prediction of discharge in Padma River by proposing PSO based novel
hybrid machine learning algorithm
Abu Reza Md. Towfiqul Islam1, Swapan
Talukdar2, Shumona Akhter1, Kutub
Uddin Eibek1, Md. Mostafizur
Rahman3, Ashraf Dewan4, Quoc Bao
Pham5,6, Nguyen Thi Thuy Linh 7,8*,
Swades Pal9, Thi Ngoc Canh DOAN10,
Duong Tran Anh11, Sobhy M. Ibrahim12,
13
This study quantified the hydrological alteration of the Padma River
basin caused by the construction of Ferakka Barrage using different
statistical models. The negative trend of the average discharge in the
dry season was detected, while the average discharge was lower than
environmental flows. PSO based ensemble machine learning models were
developed. PSO-Reduced Error Pruning Tree (REPTree), PSO-random forest
(RF), and PSO-M5P were the optimal fit for average, maximum, and minimum
discharge prediction (RMSE = 0.14, 0.3, 0.18) respectively.