Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity ana...Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on （1） a screening method （Morris） for qualitative ranking of parameters, and （2） a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model （RSMSobol）. First, the Morris screening method was used to qualitatively identify the parameters＇ sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model （RSM） were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.展开更多
A parameter estimation method based on an improved Whale Optimization Algorithm is proposed in this paper to identify the parameters of a static var compensator(SVC)model.First,a mathematical model of SVC is establish...A parameter estimation method based on an improved Whale Optimization Algorithm is proposed in this paper to identify the parameters of a static var compensator(SVC)model.First,a mathematical model of SVC is established.Then,the reverse learning strategy and Levy flight disturbance strategy are introduced to improve the whale optimization algorithm,and the improved whale optimization algorithm is applied to the parameter identification of the static var compensator model.Finally,a stepwise identification method,by analyzing the local sensitivities of parameters,is proposed which solves the problem of low accuracy caused by multi-parameter identification.This method provides a new estimation strategy for accurately identifying the parameters of the static var compensator model.Estimation results show that the parameter estimation method can be an effective tool to solve the problem of parameter identification for the SVC model.展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 41271003)the National Basic Research Program of China (Grants No. 2010CB428403 and 2010CB951103)
文摘Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on （1） a screening method （Morris） for qualitative ranking of parameters, and （2） a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model （RSMSobol）. First, the Morris screening method was used to qualitatively identify the parameters＇ sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model （RSM） were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.
文摘A parameter estimation method based on an improved Whale Optimization Algorithm is proposed in this paper to identify the parameters of a static var compensator(SVC)model.First,a mathematical model of SVC is established.Then,the reverse learning strategy and Levy flight disturbance strategy are introduced to improve the whale optimization algorithm,and the improved whale optimization algorithm is applied to the parameter identification of the static var compensator model.Finally,a stepwise identification method,by analyzing the local sensitivities of parameters,is proposed which solves the problem of low accuracy caused by multi-parameter identification.This method provides a new estimation strategy for accurately identifying the parameters of the static var compensator model.Estimation results show that the parameter estimation method can be an effective tool to solve the problem of parameter identification for the SVC model.
