Author(s): Mehdi Sheikh Goodarzi; Bahman Jabbarian Amiri; Shabnam Navardi
Linked Author(s):
Keywords: Conceptual Modeling; Global Search Algorithms (GSAs); Objective Functions (OFs); RainfallRunoff Process; Tank Hydrological Model
Abstract: Regarding to importance of modeling calibration, this study will be focused on probabilistic role of different strategies in calibration and verification steps. Tank lumped conceptual model was selected as a hydrological platform to investigate the effects of each optimization strategy on model performance. However, much considerable efforts are required to calibrate a large number of parameters in conceptual models to obtain better results. With development of artificial intelligence, three probabilistic Global Search Algorithms (GSAs) including Shuffled Complex Evolution (SCE), Genetic Algorithm (GA) and Rosenbrock Multi-Start Search (RBN) and also three Objective Functions (OFs) consisted of Nash-Sutcliffe (NSE), Root Mean Square Error (RMSE) and mean absolute error (MAE) were employed for model calibration (comparing the performance of different GSAs versus OFs). The best set of parameters, which is derived from the calibration step, will be used as prediction coefficients for the model verification stage. Performance evaluation of the simulation results was undertaken using Coefficient of Correlation (r) and Descriptive Statistics. Results indicated that all of optimization strategies have a relative ability to retrieve optimal values of eighteen parameters of the Tank model. However, the best GSAs for daily runoff simulation are SCE (0.871) and GA (0.864), respectively, for calibration and verification phases. In case of the OFs result, NSE (0.763) and RMSE (0.834) are more performant for calibration and verification of the model. Finally, the best strategy was selected by combining the results of GSAs and OFs models. Finally, SCE*MAE (0.906) and GA*RMSE (0.868) were selected as a top series.
Year: 2018