University of Tehran Press Iranian Journal of Soil and Water Research 2008-479X 51 5 2020 07 22 Investigating the Uncertainty of Data-Based Models in Forecasting Monthly Flow of the Hablehroud River Investigating the Uncertainty of Data-Based Models in Forecasting Monthly Flow of the Hablehroud River 1265 1280 75084 10.22059/ijswr.2020.286920.668288 FA Jaber Salehpoor Laghani Department of Water Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran Afhsin Ashrafzadeh Department of Water Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran Sayed Ali Moussavi Lecturer, Department of Water Engineering, University of Guilan Journal Article 2019 08 10 Accurate and reliable forecasts of river flow are required for proper management of watershed systems. In recent years, data-driven models and especially artificial intelligent based models have been successfully used in various areas related to water resources. However, uncertainty analysis of these models has been less appreciated in prior studies. In the present study, the output uncertainty of five data-driven models including modular, PCA (Principle Component Analysis), TLRN (Time-Lagged Recurrent Network), ANFIS (Adaptive-Network-based Fuzzy Inference System) and SVM (Support Vector Machine) type models in forecasting river flow has been investigated using 95PPU, p-factor and d-factor quantities. Using the observed meteorological and flow data during 1998-2012 in Hablehroud Basin, different structures of the proposed models were trained and tested. The final values of p-factor and d-factor for each model type were obtained. The results showed that SVM with a <em>p</em>-factor of 82% produces the most reliable forecasts in the present study. Accurate and reliable forecasts of river flow are required for proper management of watershed systems. In recent years, data-driven models and especially artificial intelligent based models have been successfully used in various areas related to water resources. However, uncertainty analysis of these models has been less appreciated in prior studies. In the present study, the output uncertainty of five data-driven models including modular, PCA (Principle Component Analysis), TLRN (Time-Lagged Recurrent Network), ANFIS (Adaptive-Network-based Fuzzy Inference System) and SVM (Support Vector Machine) type models in forecasting river flow has been investigated using 95PPU, p-factor and d-factor quantities. Using the observed meteorological and flow data during 1998-2012 in Hablehroud Basin, different structures of the proposed models were trained and tested. The final values of p-factor and d-factor for each model type were obtained. The results showed that SVM with a <em>p</em>-factor of 82% produces the most reliable forecasts in the present study. https://ijswr.ut.ac.ir/article_75084_694b4f64c8b2c49c2f2cebfe66f2a777.pdf