Automatic Multi-Objective Calibration of a Rainfall Runoff Model for the Fitzroy Basin, Queensland, Australia

Amir, Md. Sharif Imam Ibne; Khan, M.M.K.; Rasul, M.G.; Sharma, Raj Hari; Akram, Fatema

doi:10.7763/ijesd.2013.v4.361

Cited by 27 publications

(11 citation statements)

References 10 publications

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“…They found that a significantly a good agreement between the observed and simulated flow values. A study by Amir et al [20] at Fitzroy basin, Australia using the MIKE 11-NAM model found that there is a good hydrographs agreement between observed and simulated discharge which shows the ability of the model to simulate the streamflow the basin. Odiyo et al [25] simulated Latonyanda River Quaternary catchment (LRQ) streamflow using MIKE 11 NAM model and, found that the observed and the simulated streamflow for LRQ catchment correlated well except for under-prediction of peak events and a few low flows.…”

Section: Model Calibration and Validationmentioning

confidence: 91%

“…In manual calibration, a trial-and-error parameter adjustment is performed, based on a visual judgment by comparing the measured and the predicted discharge. Auto-calibration, the default model parameters were kept the same and the model was run in auto-calibration mode [20]. After several manual calibrations have been made the calibration was done again with very small changes.…”

Section: Calibration and Validationmentioning

confidence: 99%

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Verification of the MIKE11-NAM Model for Simulating Streamflow

Teshome¹,

Bayabil²,

Thakural³

et al. 2020

JEP

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Modeling watershed hydrological processes are important for water resources planning, development, and management. In this study, the MIKE 11-NAM (Nedbor-Afstromings Model model) was evaluated for simulation of streamflow from the Bina basin located in the Madhya Pradesh State of India. The model was calibrated and validated on a daily basis using five years (1994-1998) observed hydrological data. In addition, a model sensitivity analysis was performed on nine MIKE 11-NAM parameters to identify sensitive model parameters. Statistical and graphical approaches were used to assess the performance of the model in simulating the streamflow of the basin. Results show that during daily model calibration, the model performed very well with a coefficient of determination (R 2) and the percentage of water balance error (WBL) values 0.87% and −8.63%, respectively. In addition, the model performed good during the validation period with R 2 and WBL values of 0.68% and −6.72%, respectively. Model sensitivity analysis results showed that Overland flow runoff coefficient (CQOF), Time constant for routing overland flow (CK1,2) and Maximum water content in root zone storage (L max) were found as the most influential and sensitive model parameters for simulating streamflow. Overall, the model's performance was satisfactory based on R 2 and EI metrics.

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Section: Model Calibration and Validationmentioning

confidence: 91%

Section: Calibration and Validationmentioning

confidence: 99%

Verification of the MIKE11-NAM Model for Simulating Streamflow

Teshome¹,

Bayabil²,

Thakural³

et al. 2020

JEP

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show abstract

“…e performance of a Pareto-based 2 Computational Intelligence and Neuroscience MOABC algorithm has been investigated by Akbari et al on CEC'09 datasets [21], and the experimental results show that compared with the other multiobjective algorithms, the variants of multiobjective ABC can find solutions with competitive convergence and diversity within a shorter period of time. e parameter calibration or optimization for the hydrological models has entered the era of multiobjective optimization, and a lot of literatures focus on the multiobjective research studies [22][23][24]. For the ABC algorithm, a novel multiobjective evolutionary algorithm named multiobjective artificial bee colony (MOABC) algorithm is presented and applied in long-term cascaded hydropower system dispatch in [25].…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation Method of Multiobjective Functions’ Combination and Its Application in Hydrological Model Evaluation

Huo

Liu

2020

Computational Intelligence and Neuroscience

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Parameter optimization of a hydrological model is intrinsically a high dimensional, nonlinear, multivariable, combinatorial optimization problem which involves a set of different objectives. Currently, the assessment of optimization results for the hydrological model is usually made through calculations and comparisons of objective function values of simulated and observed variables. Thus, the proper selection of objective functions’ combination for model parameter optimization has an important impact on the hydrological forecasting. There exist various objective functions, and how to analyze and evaluate the objective function combinations for selecting the optimal parameters has not been studied in depth. Therefore, to select the proper objective function combination which can balance the trade-off among various design objectives and achieve the overall best benefit, a simple and convenient framework for the comparison of the influence of different objective function combinations on the optimization results is urgently needed. In this paper, various objective functions related to parameters optimization of hydrological models were collected from the literature and constructed to nine combinations. Then, a selection and evaluation framework of objective functions is proposed for hydrological model parameter optimization, in which a multiobjective artificial bee colony algorithm named RMOABC is employed to optimize the hydrological model and obtain the Pareto optimal solutions. The parameter optimization problem of the Xinanjiang hydrological model was taken as the application case for long-term runoff prediction in the Heihe River basin. Finally, the technique for order preference by similarity to ideal solution (TOPSIS) based on the entropy theory is adapted to sort the Pareto optimal solutions to compare these combinations of objective functions and obtain the comprehensive optimal objective functions’ combination. The experiments results demonstrate that the combination 2 of objective functions can provide more comprehensive and reliable dominant options (i.e., parameter sets) for practical hydrological forecasting in the study area. The entropy-based method has been proved that it is effective to analyze and evaluate the performance of different combinations of objective functions and can provide more comprehensive and impersonal decision support for hydrological forecasting.

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“…There have been many studies that have investigated the calibration and validation process in flood estimation (Ballesteros et al, 2011, Kundu et al, 2016, Monnier et al, 2016, Rashid et al, 2016, Viviroli et al, 2009, Yucel et al, 2015, Zhang et al, 2016 and the importance of the calibration metric on the resultant values (Amir et al, 2013, Cameron et al, 1999, Cheng et al, 2014, Cu and Ball, 2016, Liu and Sun, 2010. Also estimation of parameter value uncertainty has been conducted as part of the estimation of prediction uncertainty (Beven and Freer, 2001, Blasone et al, 2008, Del Giudice et al, 2013, Dung et al, 2015, Fan et al, 2016, Franz and Hogue, 2011, Halbert et al, 2016, Jin et al, 2010.…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation for a large catchment

Ball

2017

Australasian Journal of Water Resources

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Estimation of parameter values is an essential step in the application of catchment modelling systems. This step is time-consuming and requires considerable effort. While a variety of approaches have been developed to accelerate the process, the selection of an approach depends on the problem requiring catchment modelling, the dominant processes influencing the catchment response to storm events and, moreover, the number of parameters that need consideration. This paper will propose a method to reduce significantly the number of parameters for a large catchment when a semi-distributed catchment modelling system is applied. Past studies have reported on the use of a scaling parameter to adjust parameter values from their initial values, introduced herein is the use of a scaling parameter together with a variation coefficient. This enables the spatial variation of changes in parameter values across the catchment to be considered. A case study was conducted for a 14,000 km 2 catchment to assess the validity of this approach where the focus of the catchment modelling was the prediction of a design flood statistic. This catchment was divided into 155 subcatchments with 5 sensitive parameters per subcatchment. Hence, a total of 775 parameters needed to be considered. Using the proposed approach, the number of parameters considered during the calibration was reduced to 8 coefficients which was reasonable for a calibration and validation process that also enabled an estimate of the parameter variability.

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Automatic Multi-Objective Calibration of a Rainfall Runoff Model for the Fitzroy Basin, Queensland, Australia

Cited by 27 publications

References 10 publications

Verification of the MIKE11-NAM Model for Simulating Streamflow

Verification of the MIKE11-NAM Model for Simulating Streamflow

Evaluation Method of Multiobjective Functions’ Combination and Its Application in Hydrological Model Evaluation

Parameter estimation for a large catchment

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