How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration?

Tang, Yong; Reed, Patrick M.; Wagener, Thorsten

doi:10.5194/hess-10-289-2006

Cited by 177 publications

(75 citation statements)

References 54 publications

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“…The results from these simplified search problems can be used to successively pre-condition the search for larger, more complex formulations of ROS design problems. The ε-NSGAII, a popular multi-objective evolutionary algorithm, is chosen as it has been shown to be effective for many engineering optimization problems (Kollat and Reed, 2006;Tang et al, 2006;Kollat and Reed, 2007). For the two objectives considered in this paper, their epsilon values in ε-NSGAII (ε SI 1 and ε SI 2 ) were chosen based on reasonable and practical requirements and were both set to 0.01.…”

Section: Methodsmentioning

confidence: 99%

Improving multi-objective reservoir operation optimization with sensitivity-informed dimension reduction

Chu

Zhang

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. This study investigates the effectiveness of a sensitivity-informed method for multi-objective operation of reservoir systems, which uses global sensitivity analysis as a screening tool to reduce computational demands. Sobol's method is used to screen insensitive decision variables and guide the formulation of the optimization problems with a significantly reduced number of decision variables. This sensitivity-informed method dramatically reduces the computational demands required for attaining high-quality approximations of optimal trade-off relationships between conflicting design objectives. The search results obtained from the reduced complexity multi-objective reservoir operation problems are then used to pre-condition the full search of the original optimization problem. In two case studies, the Dahuofang reservoir and the inter-basin multi-reservoir system in Liaoning province, China, sensitivity analysis results show that reservoir performance is strongly controlled by a small proportion of decision variables. Sensitivity-informed dimension reduction and pre-conditioning are evaluated in their ability to improve the efficiency and effectiveness of multi-objective evolutionary optimization. Overall, this study illustrates the efficiency and effectiveness of the sensitivityinformed method and the use of global sensitivity analysis to inform dimension reduction of optimization problems when solving complex multi-objective reservoir operation problems.

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Section: Methodsmentioning

confidence: 99%

Improving multi-objective reservoir operation optimization with sensitivity-informed dimension reduction

Chu

Zhang

et al. 2015

Hydrol. Earth Syst. Sci.

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“…Different objective functions will result in different model parameters, thus different model performances. There are two main practices: the single-objective function and multiple-objective functions (Tang et al, 2006). Singleobjective optimization uses one objective function in the parameter optimization.…”

Section: Automated Parameter Optimizationmentioning

confidence: 99%

Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization

Chen

2016

Hydrol. Earth Syst. Sci.

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Abstract. Physically based distributed hydrological models (hereafter referred to as PBDHMs) divide the terrain of the whole catchment into a number of grid cells at fine resolution and assimilate different terrain data and precipitation to different cells. They are regarded to have the potential to improve the catchment hydrological process simulation and prediction capability. In the early stage, physically based distributed hydrological models are assumed to derive model parameters from the terrain properties directly, so there is no need to calibrate model parameters. However, unfortunately the uncertainties associated with this model derivation are very high, which impacted their application in flood forecasting, so parameter optimization may also be necessary. There are two main purposes for this study: the first is to propose a parameter optimization method for physically based distributed hydrological models in catchment flood forecasting by using particle swarm optimization (PSO) algorithm and to test its competence and to improve its performances; the second is to explore the possibility of improving physically based distributed hydrological model capability in catchment flood forecasting by parameter optimization. In this paper, based on the scalar concept, a general framework for parameter optimization of the PBDHMs for catchment flood forecasting is first proposed that could be used for all PBDHMs. Then, with the Liuxihe model as the study model, which is a physically based distributed hydrological model proposed for catchment flood forecasting, the improved PSO algorithm is developed for the parameter optimization of the Liuxihe model in catchment flood forecasting. The improvements include adoption of the linearly decreasing inertia weight strategy to change the inertia weight and the arccosine function strategy to adjust the acceleration coefficients. This method has been tested in two catchments in southern China with different sizes, and the results show that the improved PSO algorithm could be used for the Liuxihe model parameter optimization effectively and could improve the model capability largely in catchment flood forecasting, thus proving that parameter optimization is necessary to improve the flood forecasting capability of physically based distributed hydrological models. It also has been found that the appropriate particle number and the maximum evolution number of PSO algorithm used for the Liuxihe model catchment flood forecasting are 20 and 30 respectively.

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“…For example, evolutionary algorithms (EAs) were first used in hydraulic research by Babovic and Abbot's [12,18] and applied to sediment transport, salt water intrusion in estuaries, and to flow resistance stud-ies. Similarly, Tang, Reed [19] tested different multi-objective evolutionary algorithms for hydrologic model calibration, and showed that a strength Pareto evolutionary algorithm attained competitive results when used to calibrate the Sacramento soil moisture accounting model for the Leaf River watershed, and when calibrating an integrated hydrological model for the Shale Hills watershed in Pennsylvania (USA).…”

Section: Related Workmentioning

confidence: 99%

Can we obtain viable alternatives to Manning’s equation using genetic programming?

Gaitán

Balaji

Moore

2016

AIR

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Applied water research, like the one derived from open-channel hydraulics, traditionally links empirical formulas to observational data; for example Manning's formula for open channel flow driven by gravity relates the discharge (Q), cross-sectional average velocity (V ), the hydraulic radius (R), and the slope of the water surface (S) with a friction coefficient n, characteristic of the channel's surface needed in the location of interest. Here we use Genetic Programming (GP), a machine learning technique inspired by nature's evolutionary rules, to derive empirical relationships based on synthetic datasets of the aforementioned parameters. Specifically, we evaluated if Manning's formula could be retrieved from datasets with: a) 300 pentads of A, n, R, S, and Q (from Manning's equation), b) from datasets containing an uncorrelated variable and the parameters from (a), and c) from a dataset containing the parameters from (b) but using values of Q containing noise. The cross-validated results show success retrieving the functional form from the synthetic data in the first two experiments, and a more complex solution of Q for the third experiment. The results encourage the application of GP on problems where traditional empirical relationships show high biases or are non-parsimonious. The results also show alternative flow equations that might be used in the absence of one or more predictors; however, these equations should be used with caution outside of the training intervals.

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How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration?

Cited by 177 publications

References 54 publications

Improving multi-objective reservoir operation optimization with sensitivity-informed dimension reduction

Improving multi-objective reservoir operation optimization with sensitivity-informed dimension reduction

Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization

Can we obtain viable alternatives to Manning’s equation using genetic programming?

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