Parallel Shuffled Complex Evolution Algorithm for Calibration of Hydrological Models

2006 ◽  
Author(s):  
V. Sharma ◽  
D.A. Swayne ◽  
D. Lam ◽  
W. Schertzer
Keyword(s):  
Hydrological Models ◽  
Shuffled Complex Evolution ◽  
Evolution Algorithm

Usage of Differential Evolution Algorithm in the Calibration of Parametric Rainfall-Runoff Modeling

2018 ◽  
pp. 481-499 ◽  
Author(s):  
Umut Okkan ◽  
Nuray Gedik ◽  
Halil Uysal
Keyword(s):  
Climate Change ◽  
Global Optimization ◽  
Differential Evolution ◽  
Hydrological Model ◽  
Optimization Algorithms ◽  
Differential Evolution Algorithm ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Lumped Model ◽  
Evolution Algorithm

In recent years, global optimization algorithms are used in many engineering applications. Calibration of certain parameters at conceptualization of hydrological models is one example of these. An important issue in interpreting the effects of climate change on the basin depends on selecting an appropriate hydrological model. Not only climate change impact assessment studies, but also many water resources planning studies refer to such modeling applications. In order to obtain reliable results from these hydrological models, calibration phase of the models needs to be done well. Hence, global optimization methods are utilized in the calibration process. In this chapter, the differential evolution algorithm (DEA), which has rare application in the hydrological modeling literature, was explained. As an application, the use of the DEA algorithm in the hydrological model calibration phase was mentioned. DYNWBM, a lumped model with five parameters, was selected as the hydrological model. The calibration and then validation period performances of the DEA based DYNWBM model were tested and also compared with other global optimization algorithms. According to the results derived from the study, hydrological model appropriately reflects the rainfall-runoff relation of basin for both periods.

scholarly journals Does the Complexity of Evapotranspiration and Hydrological Models Enhance Robustness?

2018 ◽  
Vol 10 (8) ◽  
pp. 2837 ◽  
Author(s):  
Dereje Birhanu ◽  
Hyeonjun Kim ◽  
Cheolhee Jang ◽  
Sanghyun Park
Keyword(s):  
Potential Evapotranspiration ◽  
Estimation Method ◽  
Model Performance ◽  
Low Flow ◽  
Estimation Methods ◽  
Model Parameters ◽  
Hydrological Models ◽  
Shuffled Complex Evolution ◽  
University Of Arizona ◽  
Optimizing Model

In this study, five hydrological models of increasing complexity and 12 Potential Evapotranspiration (PET) estimation methods of different data requirements were applied in order to assess their effect on model performance, optimized parameters, and robustness. The models were applied over a set of 10 catchments that are located in South Korea. The Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm was implemented to calibrate the hydrological models for each PET input while considering similar objective functions. The hydrological models’ performance was satisfactory for each PET input in the calibration and validation periods for all of the tested catchments. The five hydrological models’ performance were found to be insensitive to the 12 PET inputs because of the SCE-UA algorithm’s efficiency in optimizing model parameters. However, the five hydrological models’ parameters in charge of transforming the PET to actual evapotranspiration were sensitive and significantly affected by the PET complexity. The values of the three statistical indicators also agreed with the computed model evaluation index values. Similarly, identical behavioral similarities and Dimensionless Bias were observed in all of the tested catchments. For the five hydrological models, lack of robustness and higher Dimensionless Bias were seen for high and low flow as well as for the Hamon PET input. The results indicated that the complexity of the hydrological models’ structure and the PET estimation methods did not necessarily enhance model performance and robustness. The model performance and robustness were found to be mainly dependent on extreme hydrological conditions, including high and low flow, rather than complexity; the simplest hydrological model and PET estimation method could perform better if reliable hydro-meteorological datasets are applied.

scholarly journals Combining an R-Based Evolutionary Algorithm and Hydrological Model for Effective Parameter Calibration

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1339 ◽  
Author(s):  
Mun-Ju Shin ◽  
Yun Choi
Keyword(s):  
Hydrological Model ◽  
Model Parameters ◽  
Model Assessment ◽  
Hydrological Models ◽  
Shuffled Complex Evolution ◽  
Physically Based ◽  
Spatially Distributed ◽  
Rainfall Runoff Model ◽  
Time Required ◽  
Runoff Model

The hydrological model assessment and development (hydromad) modeling package is an R-based package that can be applied to simulate hydrological models and optimize parameters. As the hydromad package is incompatible with hydrological models outside the package, the parameters of such models cannot be directly optimized. Hence, we proposed a method of optimizing the hydrological-model parameters by combining the executable (EXE) file of the hydrological model with the shuffled complex evolution (SCE) algorithm provided by the hydromad package. A physically based, spatially distributed, grid-based rainfall–runoff model (GRM) was employed. By calibrating the parameters of the GRM, the performance of the model was found to be reasonable. Thus, the hydromad can be used to optimize the hydrological-model parameters outside the package if the EXE file of the hydrological model is available. The time required to optimize the parameters depends on the type of event, even for the same catchment area.

A Hybrid Pso and Shuffled Complex Evolution Algorithm for Order Assignment Problem in Holonic Manufacturing System

2010 ◽  
Vol 97-101 ◽  
pp. 2213-2216 ◽  
Author(s):  
Fu Qing Zhao ◽  
Jian Hua Zou ◽  
Shang Xiong Sheng
Keyword(s):  
Assignment Problem ◽  
Lead Time ◽  
Manufacturing System ◽  
Evaluation Model ◽  
Pso Algorithm ◽  
Object A ◽  
Shuffled Complex Evolution ◽  
Evolution Algorithm ◽  
Holonic Manufacturing ◽  
Order Assignment

Today’s market dynamics have made operation process in manufacturing system extremely complex and difficult. Enterprise need to continuous adjust its strategies on evaluating and designing their task and order assignment methods to provide products at the lowest possible cost while reducing the total lead time. The evaluation model based on Holonic Manufacturing System is presented in this paper. The model incorporates operation cost and lead time as the object. A hybrid PSO algorithm with Shuffled Complex Evolution Algorithm(SCE) is utilized to compute the combination optimum for Order assignment problem . Simulation result shows that the model and the algorithm are effective to the problem.

scholarly journals Genetic algorithms for real-time operation of multipurpose water resource systems

2004 ◽  
Vol 6 (1) ◽  
pp. 19-38 ◽  
Author(s):  
Alcigeimes B. Celeste ◽  
Koichi Suzuki ◽  
Akihiro Kadota
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Real Time ◽  
Water Resource ◽  
Water Resource System ◽  
Shuffled Complex Evolution ◽  
Time Operation ◽  
Real Time Operation ◽  
Evolution Algorithm ◽  
Resource System

This paper deals with the application of genetic algorithms to the operation of a water resource system in real time. A genetic algorithm was developed and applied to solve an optimization model for the operation of the system responsible for the water supply of Matsuyama City, in Japan. For comparison purposes, the same model was solved by a technique based on calculus and the Shuffled Complex Evolution Algorithm. The general characteristics of the algorithms and the results from simulations carried out for various conditions are presented. Genetic algorithms appear to be effective tools for real-time reservoir operation.

scholarly journals Developing a shuffled complex evolution algorithm using a differential evolution algorithm for optimizing hydropower reservoir systems

2017 ◽  
Vol 18 (3) ◽  
pp. 1081-1092 ◽  
Author(s):  
Omid Bakhtiari Nezhad ◽  
Mohsen Najarchi ◽  
Mohammad Mahdi NajafiZadeh ◽  
S. Mohammad Mirhosseini Hezaveh
Keyword(s):  
Water Resources ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Mathematical Function ◽  
Complex Issue ◽  
De Algorithm ◽  
Shuffled Complex Evolution ◽  
Reservoir Systems ◽  
Evolution Algorithm ◽  
Hydropower Reservoir

Abstract The aim of this study is to improve the performance of the shuffled complex evolution (SCE) algorithm used in the optimization of hydropower generation in reservoirs as a complex issue in water resources. First, the SCE algorithm is merged with the differential evolution (DE) algorithm to form the SCE-DE algorithm. Then, a complex mathematical function is used as a benchmark to evaluate the performance and validate the SCE-DE algorithm and the outcomes are compared with the original SCE algorithm to show the superiority of the proposed SCE-DE algorithm. In addition, the two-reservoir system of Dez-Gotvand is considered as a real optimization problem to evaluate the performance of the SCE-DE algorithm. It is revealed that optimization by SCE-DE is much better than SCE. In conclusion, the results show that the proposed SCE-DE algorithm is a reasonable alternative to optimizing resource systems and can be used to solve complex issues of water resources.

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