Parameter estimation of an improved nonlinear Muskingum model using a new hybrid method

Although various techniques have been proposed to estimate the parameters of different versions of the Muskingum model, more rigorous techniques and models are still required to improve the computational precision of the calibration process. In this research, a new hybrid technique was proposed for...

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Bibliographic Details
Published in:Hydrology Research 2017-10, Vol.48 (5), p.1253-1267
Main Authors: Niazkar, Majid, Afzali, Seied Hosein
Format: Article
Language:English
Subjects:
Apis mellifera
Bees
Calibration
Case studies
Computer applications
Flood routing
Floods
Genetic algorithms
Hydraulic engineering
Hydrology
Inflow
Literature reviews
Mathematical models
Mating
Methods
Optimization
Optimization techniques
Parameter estimation
Parameters
Performance evaluation
Reproductive behaviour
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Summary:Although various techniques have been proposed to estimate the parameters of different versions of the Muskingum model, more rigorous techniques and models are still required to improve the computational precision of the calibration process. In this research, a new hybrid technique was proposed for Muskingum parameter estimation. Based on the conducted comprehensive literature review on the Muskingum flood routing models, a new improved Muskingum model with nine constant parameters was presented. Since the inflow-weighted parameter in the proposed model is a function of inflow hydrograph, it varies during the flood period and consequently can also be considered as a variable-parameter Muskingum model. The new hybrid technique was successfully applied for parameter estimation of the new version of Muskingum model for two case studies selected from the literature. Results were compared with those of other methods using several common performance evaluation criteria. The new Muskingum model significantly reduces the sum of the square of the deviations between the observed and routed outflows (SSQ) value for the double-peak case study. Finally, the obtained results indicate that not only the hybrid modified honey bee mating optimization-generalized reduced gradient algorithm somehow overcomes the shortcomings of both zero and first-order optimization techniques, but also the new Muskingum model appears to be the most reliable Muskingum version compared with the other methods considered in this study.
ISSN:0029-1277
1998-9563
2224-7955
DOI:10.2166/nh.2016.089