Runoff Estimation Using Advanced Soft Computing Techniques: A Case Study of Mangla Watershed Pakistan

A precise rainfall-runoff prediction is crucial for hydrology and the management of water resources. Rainfall-runoff a nonlinear method influenced by simulation model inputs. Previously employed methods have some limitations in predicting rainfall-runoff, such as low learning speed, overfitting issues, stopping criteria, back-propagation issues. Therefore, this study uses distinctive soft computing approaches to overcome these issues modeling Mangla watershed Pakistan. data 29 years from 1978–2007 used estimate runoff. The are Tree Boost (TB), decision tree forests (DTFs), single trees (SDTs). Using various combinations past rainfall datasets, techniques validated tested security efficient results. evaluation criteria models statistical measures consisting root means square error (RMSE), mean absolute (MAE), coefficient determination (R2), Nash–Sutcliffe efficiency (NSE). outcomes were evaluated with multilayer perceptron (MLP). DTF was found be more accurate approach average parameters R2, NSE, RMSE, MAE being 0.9, 0.8, 1000, 7000 cumecs. Regarding R2 there about 57% 17% improvement results compared other techniques. Flow duration curves (FDCs) revealed that performed better than This assessment has potential; researchers may consider it an alternative estimations watershed.