In this study, the capability of two different types of models including Hydrological Simulation Program-Fortran (HSPF) as a process-based model and ANN as a data-driven model in simulating runoff was evaluated. The c...In this study, the capability of two different types of models including Hydrological Simulation Program-Fortran (HSPF) as a process-based model and ANN as a data-driven model in simulating runoff was evaluated. The considered area is the Balkhichai River watershed in northwest of Iran. HSPF is a semi-distributed deterministic, continuous and physically-based model that can simulate the hydrologic cycle, associated water quality and quantity and process on pervious and impervious land surfaces and streams. Artificial neural network (ANN) is probably the most successful learning machine technique with flexible mathematical structure which is capable of identifying complex non-linear relationships between input and output data without attempting to reach the understanding of the nature of the phenomena. Statistical approach depending on cross-, auto- and partial-autocorrelation of the observed data is used as a good alternative to the trial and error method in identifying model inputs. The performances of ANN and HSPF models in calibration and validation stages are compared with the observed runoff values in order to identify the best fit forecasting model based upon a number of selected performance criteria. Results of runoff simulation indicated that the simulated runoff by ANN was generally closer to the observed values than those predicted by HSPF.展开更多
文摘In this study, the capability of two different types of models including Hydrological Simulation Program-Fortran (HSPF) as a process-based model and ANN as a data-driven model in simulating runoff was evaluated. The considered area is the Balkhichai River watershed in northwest of Iran. HSPF is a semi-distributed deterministic, continuous and physically-based model that can simulate the hydrologic cycle, associated water quality and quantity and process on pervious and impervious land surfaces and streams. Artificial neural network (ANN) is probably the most successful learning machine technique with flexible mathematical structure which is capable of identifying complex non-linear relationships between input and output data without attempting to reach the understanding of the nature of the phenomena. Statistical approach depending on cross-, auto- and partial-autocorrelation of the observed data is used as a good alternative to the trial and error method in identifying model inputs. The performances of ANN and HSPF models in calibration and validation stages are compared with the observed runoff values in order to identify the best fit forecasting model based upon a number of selected performance criteria. Results of runoff simulation indicated that the simulated runoff by ANN was generally closer to the observed values than those predicted by HSPF.
