Application of New Hybrid Optimization Technique for Parameter Estimation of New Improved Version of Muskingum Model

Niazkar, Majid; Afzali, Seied Hosein

doi:10.1007/s11269-016-1449-9

Cited by 45 publications

(20 citation statements)

References 35 publications

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“…The hybrid MHBMO-GRG algorithm was first suggested by Niazkar and Afzali [28] and has been successfully applied for solving some problems in water engineering field [21][22][28][29][30]. This hybrid algorithm comprises search-based and deterministic optimization algorithms used in two consecutive steps.…”

Section: The Hybrid Mhbmo-grg Algorithmmentioning

confidence: 99%

“…However, the MHBMO-GRG hybrid method overcomes these drawbacks. According to the successful experience of applying this hybrid method for solving several problems [21][22][28][29][30], it should be mentioned that not only the new hybrid method enhances the applicability of the MHBMO algorithm in finding global optimum values, but also it adequately provides a set of initial guesses for the GRG algorithm.…”

Section: The Hybrid Mhbmo-grg Algorithmmentioning

confidence: 99%

“…In this paper, two new explicit equations are developed for estimating bridge afflux using Genetic Algorithm (GA) and Modified Honey Bee Mating Optimization-Generalized Reduced Gradient (MHBMO-GRG) Algorithm. These algorithms have been successfully utilized for solving various civil and water engineering problems in the literature [21][22][28][29][30] while it is the first time that they have been applied to estimate bridge backwater depth to the authors' knowledge. Moreover, Genetic Programming (GP) has been used for similar purpose for the first time in the literature.…”

Section: Introductionmentioning

confidence: 99%

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Bridge backwater estimation: A Comparison between artificial intelligence models and explicit equations

2020

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Estimation of bridge backwater has been one of practical challenges in hydraulic engineering for decades. In this study, Genetic Programming (GP) has been applied for estimating bridge backwater for the first time based on the conducted literature review. Furthermore, two new explicit equations are developed for predicting bridge afflux using Genetic Algorithm (GA) and hybrid MHBMO-GRG algorithm. The performances of these models are compared with Artificial Neural Network (ANN) and several explicit equations available in the literature considering both laboratory and field data. Based on five considered performance evaluation criteria, the two new explicit equations outperform the ones available in the literature. Furthermore, GP and ANN achieve the 2 best results in favor of four out of five considered criteria for train and test data, respectively. To be more specific, ANN improves the MSE and R 2 values of the explicit equation developed using GA by 44% and 12% for the test data while GP enhances the corresponding values by 62% and 9% for the train data. Finally, the results demonstrate that not only artificial intelligence models considerably improve bridge afflux estimation than the explicit equations but also the suggested equations significantly improve the accuracy of the available explicit ones.

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Section: The Hybrid Mhbmo-grg Algorithmmentioning

confidence: 99%

Section: The Hybrid Mhbmo-grg Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bridge backwater estimation: A Comparison between artificial intelligence models and explicit equations

2020

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“…In other words, the OUR belongs to unsteady flow, which can be describe the Saint-Venant equations. Due to the low solving efficiency and the high requirements for the topography and roughness data, many hydrologic streamflow routing methods, such as the Muskingum model which is the most extensively studied and utilized, have been put forward for the streamflow routing in the river channel [17], [21]. Muskingum was proposed by MC.Carthy in 1938 and named after its use in the Muskingum river in the United States [21].…”

Section: B Muskingum Modelmentioning

confidence: 99%

“…Changming Ji and Chuangang Li selected the Muskingum model, which is most widely used in SRMH, and combined it with multidimensional DP algorithm to solve the SGDCR of Jindong and Guandi hydropower stations successfully, which are located in Sichuan province in southwestern China [17]. The Muskingum model holds that the streamflow propagation between cascade reservoirs can be generalized as not only transposition but also attenuation [17] and assumes that there is a one-to-one relationship between the river channel storage and the streamflow at a random section [19]- [21]. As a simplified model of HNS, the Muskingum model remains efficient in characterizing the hydraulic connection as the LT model.…”

Section: Introductionmentioning

confidence: 99%

Characterizing the Hydraulic Connection of Cascade Reservoirs for Short-Term Generation Dispatching via Gaussian Process Regression

et al. 2020

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The characterization of the mapping relationship (MR) between outflow of the upstream reservoir (OUR) and inflow of the downstream reservoir (IDR) in the short-term generation dispatching of cascade reservoirs (SGDCR) greatly impacts the safe and economic operation of hydropower plants. If this MR is not characterized properly, the operation process of hydropower stations will deviate from the planning dispatching schemes. Especially when the upstream reservoir (UR) undertakes peak load regulation tasks frequently and the downstream reservoir (DR) owns weak re-regulation capacity, the safety of cascade reservoirs will be seriously threatened. In this extreme system, the commonest characterizing models in SGDCR, such as the lag time (LT) model and the Muskingum model, may cause a huge deviation when simulating or forecasting the IDR. Given this dilemma, the Gaussian process regression (GPR) model, which is a representative for Bayesian regression methods, is firstly introduced to handle this MR and compared with the LT and the Muskingum model in this paper. The Three Gorges-Gezhouba cascade reservoirs (TGGCR) in China are selected as a typical case study. The performance indicators of four categories are adopted to evaluate the simulated IDR in a single period and a set of pivotal indicators are proposed to estimate the simulation dispatching in multiple periods. The results show that (1) The GPR model reduces the mean absolute deviation (MAD) about inflow of Gezhouba by 197m 3 /s and 263m 3 /s than the LT and the Muskingum model respectively; (2) The distribution characteristics of inflow deviation produced by the GPR model are most competitive. Meanwhile, the GPR model has the strongest ability when conducting the multi-period simulation dispatching and owns best applicability on the whole range of streamflow. (3) The Muskingum model is not recommended to characterize the hour-scale hydraulic connection in the extreme system of SGDCR. INDEX TERMS cascade reservoirs, short-term generation dispatching, hour-scale hydraulic connection, Gaussian process regression.

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Parameter Estimation of a Nonlinear Hydrologic Model for Channel Flood Routing with the Bat Algorithm

Sánchez

Suárez

Gálvez

et al. 2019

Lecture Notes in Computer Science

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Application of New Hybrid Optimization Technique for Parameter Estimation of New Improved Version of Muskingum Model

Cited by 45 publications

References 35 publications

Bridge backwater estimation: A Comparison between artificial intelligence models and explicit equations

Bridge backwater estimation: A Comparison between artificial intelligence models and explicit equations

Characterizing the Hydraulic Connection of Cascade Reservoirs for Short-Term Generation Dispatching via Gaussian Process Regression

Parameter Estimation of a Nonlinear Hydrologic Model for Channel Flood Routing with the Bat Algorithm

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