Comparison of Stochastic Optimization Algorithms in Hydrological Model Calibration

Arsenault, R.J.; Poulin, Annie; Côté, Pascal; Brissette, François

doi:10.1061/(asce)he.1943-5584.0000938

Cited by 195 publications

(176 citation statements)

References 42 publications

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“…Generally, an RR model is linked to stochastic optimization algorithms to evaluate the fitness of potential parameter sets and to identify the optimal parameter set resulting in the best accuracy for the model [13]. These algorithms include simulated annealing (SA) [14], genetic algorithm (GA) [15,16], particle swarm optimization (PSO) [17], harmony search (HS) [18], and shuffled complex evolution-University of Arizona (SCE-UA) [12].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Automatic Parameter Calibration of a Hydrological Model

Jung

Choi

Kim

2017

Water

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This study proposes variable balancing approaches for the exploration (diversification) and exploitation (intensification) of the non-dominated sorting genetic algorithm-II (NSGA-II) with simulated binary crossover (SBX) and polynomial mutation (PM) in the multiobjective automatic parameter calibration of a lumped hydrological model, the HYMOD model. Two objectives-minimizing the percent bias and minimizing three peak flow differences-are considered in the calibration of the six parameters of the model. The proposed balancing approaches, which migrate the focus between exploration and exploitation over generations by varying the crossover and mutation distribution indices of SBX and PM, respectively, are compared with traditional static balancing approaches (the two dices value is fixed during optimization) in a benchmark hydrological calibration problem for the Leaf River (1950 km 2 ) near Collins, Mississippi. Three performance metrics-solution quality, spacing, and convergence-are used to quantify and compare the quality of the Pareto solutions obtained by the two different balancing approaches. The variable balancing approaches that migrate the focus of exploration and exploitation differently for SBX and PM outperformed other methods.

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Section: Introductionmentioning

confidence: 99%

Multiobjective Automatic Parameter Calibration of a Hydrological Model

Jung

Choi

Kim

2017

Water

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“…During the last decade a number of papers aimed at comparison among optimization algorithms applied to rainfall-runoff model calibration have been published (Goswami and O'Connor 2007;Arsenault et al 2014, Piotrowski et al 2017a. Such studies generally showed that many algorithms perform similarly well and no best method may be determined.…”

Section: Introductionmentioning

confidence: 99%

“…In some hydrological studies, the relation between the performance of the conceptual rainfall-runoff model and the number of function calls is considered, often referring to the graphically-illustrated convergence speed (Tolson and Shoemaker 2007;Arsenault et al 2014;Piotrowski et al 2017a). In such figures the relation between the quality measure of the best solution found so far is plotted against the already performed number of function calls.…”

Section: Introductionmentioning

confidence: 99%

“…However, from such illustrations published in various studies aiming at rainfall-runoff modeling different conclusions could be drawn. From Arsenault et al (2014) one may learn that some algorithms initially converge quicker than the others, but such methods often perform worse than the best ones at the end of the search. A similar result was obtained by Jeon et al (2014), but for another goal, namely calibration of long-term hydrologic impact assessment model.…”

Section: Introductionmentioning

confidence: 99%

“…In Arsenault et al (2014) study one may also find another approach to this topic. It shows after how many function calls the particular algorithm on average reaches the 95% of the best value of the Nash-Sutcliffe coefficient (Nash and Sutcliffe 1970) found during the whole search (e.g.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Relationship Between Calibration Time and Final Performance of Conceptual Rainfall-Runoff Models

Piotrowski

Napiorkowski

Osuch

2018

Water Resour Manage

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Various methods are used in the literature for calibration of conceptual rainfall-runoff models. However, very rarely the question on the relation between the number of model runs (or function calls) and the quality of solutions found is asked. In this study two lumped conceptual rainfall-runoff models (HBV and GR4J with added snow module) are calibrated for five catchments, located in temperate climate zones of USA and Poland, by means of three modern variants of Evolutionary Computation and Swarm Intelligence optimization algorithms with four different maximum numbers of function calls set to 1000, 3000, 10,000 and 30,000. At the calibration stage, when more than 10,000 function calls is used, only marginal improvement in model performance has been found, irrespective of the catchment or calibration algorithm. For validation data, the relation between the number of function calls and model performance is even weaker, in some cases the longer calibration, the poorer modelling performance. It is also shown that the opinion on the model performance based on different popular hydrological criteria, like the Nash-Sutcliffe coefficient or the Persistence Index, may be misleading. This is because very similar, largely positive values of Nash-Sutcliffe coefficient obtained on different catchments may be accompanied by contradictory values of the Persistence Index.

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Nonlinear time‐series modeling of unconfined groundwater head

Peterson

Western

2014

Water Resources Research

101

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This paper presents a nonlinear transfer function noise model for time-series modeling of unconfined groundwater hydrographs. The motivation for its development was that existing groundwater time-series models were unable to simulate large recharge events and multiyear droughts. This was because existing methods do not partition rainfall to runoff and do not account for nonlinear soil water drainage. To account for these nonlinear processes, a vertically integrated soil moisture module was added to an existing transfer function noise model. The soil moisture module has a highly flexible structure that allowed 84 different forms to be built. Application of the time-series model requires numerical calibration of parameters for the transfer functions, noise model and, for the nonlinear models, the soil moisture module. This was undertaken using the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) global calibration scheme. However, reproducible calibration to the global optima was challenging and a number of modifications were required to the transfer function noise model. In trialing the 84 nonlinear models and 2 linear models, each was applied to eleven observation bores within a paired catchment study area in Great Western, Victoria, Australia. In comparison with existing groundwater hydrograph time-series models, the proposed nonlinear time-series model performed significantly better at all observation bores during calibration and evaluation periods. Both the linear and nonlinear models were also used to quantify the impact of revegetation within the paired catchment; however, results were inconclusive, which is likely due to time-series data for the state of the revegetation being unavailable. By analyzing the application of 84 nonlinear models to each bore, an optimal structure for the soil moisture module was identified. It is unlikely, however, that this model structure would be appropriate for all climates and geologies. To encourage further investigations, open-source code for the highly flexible groundwater time-series modeling framework is available and we invite others to develop new models.

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Comparison of Stochastic Optimization Algorithms in Hydrological Model Calibration

Cited by 195 publications

References 42 publications

Multiobjective Automatic Parameter Calibration of a Hydrological Model

Multiobjective Automatic Parameter Calibration of a Hydrological Model

Relationship Between Calibration Time and Final Performance of Conceptual Rainfall-Runoff Models

Nonlinear time‐series modeling of unconfined groundwater head

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