Calibration of a modified SFB model for twenty-five Australian catchments using simulated annealing

Sumner, Neil R.; Fleming, P.M.; Bates, Bryson C.

doi:10.1016/s0022-1694(96)03277-5

Cited by 54 publications

(36 citation statements)

References 23 publications

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“…1. Types of error between estimated and observed discharge hydrographs after Serban and Askew (1991) and Parent, 1998;Mroczkowski et al, 1997;Ye et al, 1997;Franchini and Galeati, 1997;Yapo et al, 1996;Gan et al, 1997;Lauzon et al, 1997;Lamb, 1997;Sumner et al, 1997;Cooper et al, 1997;Todini, 1996). Although considered of secondary importance, notable attention has also been focussed on procedures for updating the substantive model forecasts, particularly by international hydrological organisations such as the World Meteorological Organisation (WMO) (WMO, 1992).…”

Section: Introductionmentioning

confidence: 99%

A non-linear neural network technique for updating of river flow forecasts

Shamseldin

O’Connor

2001

Hydrol. Earth Syst. Sci.

100

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A non-linear Auto-Regressive Exogenous-input model (NARXM) river flow forecasting output-updating procedure is presented. This updating procedure is based on the structure of a multi-layer neural network. The NARXM-neural network updating procedure is tested using the daily discharge forecasts of the soil moisture accounting and routing (SMAR) conceptual model operating on five catchments having different climatic conditions. The performance of the NARXM-neural network updating procedure is compared with that of the linear Auto-Regressive Exogenous-input (ARXM) model updating procedure, the latter being a generalisation of the widely used Auto-Regressive (AR) model forecast error updating procedure. The results of the comparison indicate that the NARXM procedure performs better than the ARXM procedure.

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Section: Introductionmentioning

confidence: 99%

A non-linear neural network technique for updating of river flow forecasts

Shamseldin

O’Connor

2001

Hydrol. Earth Syst. Sci.

100

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“…Simulated annealing has been used for optimal selection of the number and location of rainfall gauges for rainfall estimation (Pardo-Igúzquiza, 1998), and simulated annealing combined with the simplex method was used for the calibration of a semi-distributed model for conjunctive simulation of surface and groundwater flows (Rozos et al, 2004), for optimization of a conceptual rainfall-runoff model (Thyer et al, 1999;Sumner et al, 1997), and for the estimation of baseflow parameters of the ARNO model (Abdulla et al, 1999).…”

Section: Simulated Annealing (Sa)mentioning

confidence: 99%

Comparative assessment of six automatic optimization techniques for calibration of a conceptual rainfall—runoff model

Goswami

O’Connor

2007

Hydrological Sciences Journal

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In this application-based study, six automated strategies of parameter optimization are used for calibration of the conceptual soil moisture accounting and routing (SMAR) model for rainfall-runoff simulation in two catchments, one small and the other large. The methods used are: the genetic algorithm, particle swarm optimization, Rosenbrock's technique, shuffled complex evolution of the University of Arizona, simplex search, and simulated annealing. A comparative assessment is made using the Nash-Sutcliffe model efficiency index and the mean relative error (MRE) to evaluate the performance of each optimization method. It is found that the degree of variation of the values of the water balance parameters is generally less for the small catchment than for the large one. In the case of both catchments, the probabilistic global population-based optimization method of simulated annealing is considered best in terms of having the least variability of parameter values in successive tests, thereby alleviating the phenomenon of equifinality in parameter optimization, and also in producing the lowest MRE in verification.Key words optimization; SMAR; genetic algorithm; Rosenbrock; simplex; particle swarm optimization; simulated annealing; shuffled complex evolution Evaluation comparative de six techniques automatiques d'optimisation pour le calage d'un modèle conceptuel pluie-débitRésumé Dans cette étude de mise en application, six stratégies automatiques d'optimisation de paramètres sont utilisées pour le calage du modèle conceptuel "soil moisture accounting and routing" (SMAR) pour la simulation pluie-débit dans deux bassins versants, l'un petit et l'autre grand. Les méthodes utilisées sont: l'algorithme génétique, l'optimisation par essaim de particules, la technique de Rosenbrock, la technique "shuffled complex evolution" de l'Université d'Arizona, la méthode du simplex et celle du recuit simulé. Une estimation comparative est menée à l'aide de l'indice d'efficience de modélisation de Nash-Sutcliffe et de l'erreur relative moyenne pour évaluer la performance de chaque méthode d'optimisation. Il apparaît que le degré de variation des valeurs des paramètres du bilan hydrologique est généralement inférieur pour le petit bassin versant que pour le grand. Dans le cas de l'ensemble des deux bassins, la méthode d'optimisation du recuit simulé, probabiliste globale basée sur la population, apparaît être la meilleure en termes de moindre variabilité des valeurs des paramètres au cours de tests successifs, ce qui atténue le phénomène d'équifinalité vis-à-vis de l'optimisation des paramètres et également de la minimisation de l'erreur relative moyenne lors de la vérification.

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“…Some of the automatic optimization methods that have calibrated Xinanjiang model are genetic algorithm (Cheng et al, 2006), shuffled complex evolution (SCE) algorithm (Duan et al, 1992(Duan et al, , 1994) and simulated annealing (Sumner et al, 1997). www.ijacsa.thesai.org Among the Global Optimization Methods, Kuok (2010) found that Particle Swarm Optimization method (PSO) is more reliable and promising to provide the best fit between the observed and simulated runoff.…”

Section: Introductionmentioning

confidence: 99%