Comparison of efficiency between differential evolution and evolution strategy: application of the LST model to the Be River catchment in Vietnam

Nguyen Thi Thuy Hang, Hidetaka Chikamori

Research output: Contribution to journalArticle

Abstract

Parameter calibration is an important step in the development of rainfall–runoff models. Recently, there has been a significant focus on automatic calibration. In this paper, two evolutionary optimization algorithms were applied to calibration of the long- and short-term runoff model (LST model) to simulate the daily rainfall–runoff process in the Be River catchment located in southern Vietnam. The differential evolution (DE) and evolution strategy (ES) algorithms were employed to optimize three objective functions: the Nash–Sutcliffe efficiency coefficient, root mean square error, and mean absolute error, which are indices for evaluating the simulation accuracy of the LST model. Hydrometeorological data for the periods 1985–1989 and 1990–1991 were used for calibration and validation, respectively. The LST model was calibrated for each objective function using five different parent and offspring population conditions. The results show that both the DE and ES algorithms are efficient methods for automatic calibration of the LST model. After 1000 generations, the best values of the fitness indices found by the DE technique were slightly better and more stable than those found by the ES technique in both calibration and validation. The average computation time for each generation using the DE algorithm was approximately two-thirds as long as that using the ES algorithm.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalPaddy and Water Environment
DOIs
Publication statusAccepted/In press - Apr 10 2017

Fingerprint

hydrologic models
Runoff
Vietnam
Catchments
Rivers
Calibration
catchment
runoff
rivers
calibration
river
Mean square error
comparison
fitness
methodology
simulation

Keywords

  • Differential evolution
  • Evolution strategy
  • LST model
  • Optimization
  • Parameter calibration
  • Rainfall–runoff model

ASJC Scopus subject areas

  • Environmental Engineering
  • Water Science and Technology
  • Agronomy and Crop Science

Cite this

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abstract = "Parameter calibration is an important step in the development of rainfall–runoff models. Recently, there has been a significant focus on automatic calibration. In this paper, two evolutionary optimization algorithms were applied to calibration of the long- and short-term runoff model (LST model) to simulate the daily rainfall–runoff process in the Be River catchment located in southern Vietnam. The differential evolution (DE) and evolution strategy (ES) algorithms were employed to optimize three objective functions: the Nash–Sutcliffe efficiency coefficient, root mean square error, and mean absolute error, which are indices for evaluating the simulation accuracy of the LST model. Hydrometeorological data for the periods 1985–1989 and 1990–1991 were used for calibration and validation, respectively. The LST model was calibrated for each objective function using five different parent and offspring population conditions. The results show that both the DE and ES algorithms are efficient methods for automatic calibration of the LST model. After 1000 generations, the best values of the fitness indices found by the DE technique were slightly better and more stable than those found by the ES technique in both calibration and validation. The average computation time for each generation using the DE algorithm was approximately two-thirds as long as that using the ES algorithm.",
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AB - Parameter calibration is an important step in the development of rainfall–runoff models. Recently, there has been a significant focus on automatic calibration. In this paper, two evolutionary optimization algorithms were applied to calibration of the long- and short-term runoff model (LST model) to simulate the daily rainfall–runoff process in the Be River catchment located in southern Vietnam. The differential evolution (DE) and evolution strategy (ES) algorithms were employed to optimize three objective functions: the Nash–Sutcliffe efficiency coefficient, root mean square error, and mean absolute error, which are indices for evaluating the simulation accuracy of the LST model. Hydrometeorological data for the periods 1985–1989 and 1990–1991 were used for calibration and validation, respectively. The LST model was calibrated for each objective function using five different parent and offspring population conditions. The results show that both the DE and ES algorithms are efficient methods for automatic calibration of the LST model. After 1000 generations, the best values of the fitness indices found by the DE technique were slightly better and more stable than those found by the ES technique in both calibration and validation. The average computation time for each generation using the DE algorithm was approximately two-thirds as long as that using the ES algorithm.

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