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Water 2017, 9(7), 495;

Application of Multi-Step Parameter Estimation Method Based on Optimization Algorithm in Sacramento Model

Institute of Water Resources and Hydro-Electric Engineering, Xi’an University of Technology, Xi’an 710048, China
State Grid Gansu Electric Power Company, Gansu Electric Power Research Institute, Lanzhou 730050, China
Author to whom correspondence should be addressed.
Received: 4 May 2017 / Revised: 24 June 2017 / Accepted: 3 July 2017 / Published: 7 July 2017
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The Sacramento model is widely utilized in hydrological forecast, of which the accuracy and performance are primarily determined by the model parameters, indicating the key role of parameter estimation. This paper presents a multi-step parameter estimation method, which divides the parameter estimation of Sacramento model into three steps and realizes optimization step by step. We firstly use the immune clonal selection algorithm (ICSA) to solve the non-liner objective function of parameter estimation, and compare the parameter calibration result of ideal artificial data with Shuffled Complex Evolution (SCE-UA), Parallel Genetic Algorithm (PGA), and Serial Master-slaver Swarms Shuffling Evolution Algorithm Based on Particle Swarms Optimization (SMSE-PSO). The comparison result shows that ICSA has the best convergence, efficiency and precision. Then we apply ICSA to the parameter estimation of single-step and multi-step Sacramento model and simulate 32 floods based on application examples of Dongyang and Tantou river basins for validation. It is clearly shown that the results of multi-step method based on ICSA show higher accuracy and 100% qualified rate, indicating its higher precision and reliability, which has great potential to improve Sacramento model and hydrological forecast. View Full-Text
Keywords: sacramento model; ICSA; parameter estimation; multi-step sacramento model; ICSA; parameter estimation; multi-step

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Zhang, G.; Xie, T.; Zhang, L.; Hua, X.; Liu, F. Application of Multi-Step Parameter Estimation Method Based on Optimization Algorithm in Sacramento Model. Water 2017, 9, 495.

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