径流分类组合预报方法及其应用研究
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摘要
我国是一个水资源贫乏、洪水灾害频发的国家,如何利用现有的水利工程,在保证防洪安全的前提下,通过采取适当的非工程措施达到经济、合理利用有限的水资源、提高水能利用率便成为当前亟待解决的问题。在众多的非工程措施中,径流预报与优化调度是保证水电站水库防洪安全与提高经济效益的有效途径之一,本文以此为切入点,以桓仁水电站与二滩水电站为研究对象,围绕径流预报与水电站预报优化调度的若干关键技术进行了较为深入的探讨与研究。构建一个中长期径流的年内分段组合预报模型,该模型能够综合利用多种智能方法的预报优势进行分类径流预报,以此预报结果为输入,研究了基于不同径流描述的水电站中长期预报优化调度模型及模糊风险计算新方法。对于水电站的短期洪水预报而言,流域洪水预报模型的不确定性已受到普遍的关注,本文以新安江模型为例,对模型参数优选、模型输入与模型参数不确定性进行了深入的分析,提出了模糊分类洪水组合预报方法。主要研究内容与成果如下:
(1)针对以往中长期径流预报通常仅采用单一方法进行建模与预报,难以利用各预报方法优势的缺陷,本文构建了一个中长期径流的年内分阶段组合预报模型。模型选用多种预报方法进行组合预报,从而发挥各方法的预报优势以达到提高预报精度的目的。针对年内不同时期径流成因的差异,将年内径流分为多个阶段,对各阶段分别建立相应的组合预报模型。特别针对径流受降雨因素影响较大的阶段,其预报不确定性增强的特点,引入贝叶斯平均(BMA)组合预报模型,可同时发布确定性预报与概率预报。通过对二滩水电站历史月径流的模拟预报,表明分段组合预报模型较单一预报方法能够提高确定性预报精度,且发布的概率预报较为可靠,可为水电站预报调度提供决策支持。
(2)为合理利用河川径流,提高水电站的水能利用率,将径流分段组合预报模型应用于水电站的中长期优化调度中,针对利用预报信息指导优化调度所带来的风险,研究了水电站中长期发电调度的模糊风险计算新方法。方法主要包括基于分段组合预报模型的年内月径流递推组合预报方法:以年发电量最大作为优化目标的水电站优化调度模型。以组合预报模型提供的不同径流描述作为水电站的预报来水,以优化调度模型求解调度决策。为量化预报误差可能对发电调度带来的风险,研究了水电站中长期调度的发电模糊风险计算新方法,较常规风险计算方法能够合理的描述风险的模糊性并量化风险损失的程度。通过对二滩水电站典型年的模拟调度,表明将分段组合预报模型应用于水电站的中长期优化调度中是可行的、有效的,模糊风险分析方法能够较常规方法更合理的描述风险从而便于决策。
(3)针对新安江模型参数优选中存在的不确定性问题,提出了模型参数的两阶段多目标优化率定方法。首先为便于分析目标权重对率定结果的影响,解决应用随机模拟方法求解该问题时造成计算量巨大的瓶颈,在第一阶段采用MOSCEM-UA算法进行参数优选,以此作为分析对象,从而有效缩减了求解问题的规模和计算量,在第二阶段通过分析目标权重对模型参数优选的影响,建立了基于排序可靠度特征值的满意参数优选方法。针对目标权重的确定,将数学与经验权重进行组合确定目标组合权重,同时分析了不同的数学权重计算方法与不同优选目标偏好对参数优选结果的影响。实例分析表明,该方法能够在综合评价参数率定目标特征值与排序可靠度的基础上选择折衷满意模型参数,并能够提供相应的排序不确定性信息。
(4)对新安江模型的参数与输入不确定性进行了研究。以往研究多集中于对模型参数的不确定性分析上,而对模型输入的不确定性研究较少,因此,以模型参数与模型输入不确定性作为研究对象,采用SCEM-UA方法对场次洪水模拟不确定性进行了分析。与普遍采用的GLUE方法相比,SCEM-UA方法无需主观定义似然函数临界值,采用算法收敛后的非劣参数作为可行参数。通过对桓仁水库场次洪水模拟的不确定性分析,表明模型参数与输入对洪水模拟结果有显著的影响,且后者的影响较大,因此,在实时洪水预报时应对此予以充分考虑以量化预报风险。
(5)针对洪水预报模型存在的“异参同效”问题,为提高模型的预报精度与可靠度,研究了模糊分类洪水组合预报方法。该方法在对洪水进行模糊分类的基础上,对各类洪水分别进行预报模型参数率定,从而提高了场次洪水模拟精度。通过利用同类洪水中具有“同效”现象的多组参数进行洪水预报,采用相应的组合方法进行分类内预报结果组合,进而再对各分类的预报结果进行模糊加权组合得到洪水组合预报值。实例应用表明,该方法能够提高场次洪水预报精度。将模糊分类洪水组合预报方法应用于实时洪水预报调度中,与采用一组满意参数进行洪水预报方法相比,在一些大量级场次洪水中可使水库提前加大泄流量从而有效降低调洪最高水位并减小水库的防洪风险。
最后对全文做了总结,并对有待于进一步研究的问题进行了展望。
With the aggravation of the water resources scarcity and frequently flood disaster, it's urgent to take certain measures for utilizing limited water resources and improving the efficiency of the hydropower with the current hydraulic engineering under the safety of flood control. Hydrological prediction and optimal operation is one of the effective solutions for flood protection and can improve the benefit for hydropower station. Therefore, based on the Huanren hydropower station and Ertan hydropower station, some key problems for the runoff prediction and optimal operation of the hydropower station are studied. In this dissertation, firstly, the ensemble prediction model by stages (EPMS) is developed for medium-long term hydrological prediction, the model developed utilizes the advantages of various traditional and intelligent methods for runoff prediction, and then, based on the different runoff description, a new method for hydropower station optimal operation and risk analysis is presented. As for the flood forecasting, much attention have been put on the uncertainty of the hydrological model, therefore, take the Xinanjiang model as an example, this dissertation studies the uncertainty resulting from the optimal selection, model parameters calibration and model input error. Further, the method for flood ensemble forecasting based on the fuzzy classification is developed. The major research work is outlined as follows:
(1) The ensemble prediction model by stages is presented for medium-long term hydrological prediction to overcome the disadvantages of the single method. The model developed using a number of models for streamflow forecasting so as to improving the forecasting accuracy. Considering the differences of the runoff formation, the year is divided into several periods, then, the ensemble model for each periods is established. Due to the uncertainty of the precipitation, the Bayesian Model Averaging (BMA) is employed as the ensemble model for certain period, which can release both deterministic and probabilistic forecast. Take the Ertan hydropower station as the study case, results indicate the model proposed is superior to the single forecasting model and the probabilistic prediction is reliable which can support decision making.
(2) To impove the waterpower utilization ratio, EPMS is employed in the medium-long term optimal operation for hydropower station. For quantifing the power generation risk due to the flow prediction error, a noval method called fuzzy risk analysis is developed. The method proposed includes: monthly streamflow recursive prediction using the EPMS; Optimal operation model with the object of maximum the annual power generation, which uses the prediction of different runoff description provided by EPMS; Fuzzy risk analysis method for the power generation. The method is applied to the Ertan hydropower station located in the Yalong river, the operation results for the typical years indicate the effectiveness and efficiency of the model, and the fuzzy risk analysis method can describe the risk more reasonabily than commom method.
(3) Aiming at the uncertainty of the Xinanjiang hydrological model parameters selection, a noval two-stage parameters calibration method is presented. In the first stage, in order to analyzed the sensitivity of the objective weights which will lead to bottleneck when employs the stochastic simulation method, the MultiObjective Shuffled Complex Evolution Metropolis (MOSCEM-UA) algorithm is employed for searching the non-dominated solutions, thus the computational complexity can be reduced effectively, further, the satisfactory solution is determined using the sorting characteristic value considering the ranking reliability in the second stage. Furthermore, the influence of using different methods for calculating the mathematical weights and combination weights on the alternatives ranking is investigated. The study case demonstrates the proposed method can provide both satisfactory parameters and ranking reliability.
(4) Predictive uncertainty analysis aiming at the parameters and input of the Xinanjiang model is presented. In this study, the predictive uncertainty analysis considering the model parameters and model input using the Shuffled Complex Evolution Metropolis (SCEM-UA) algorithm is proposed. Compared with the GLUE method, SCEM-UA algorithm doesn't require determine the value of the cut-off threshold for dividing the behavior and non-behavior parameter set, it only using the non-inferior parameters for flood simulation based on the appropriate converage of the algorithm. Taking the Huanren reservoir as an example, shows the model parameters and model input have the significant influence on the rainfall-runoff simulation, and the latter is larger than the former. Therefore, the predictive uncertainty should be fully considered during the real time flood forecasting.
(5) Aiming at the equifinality of the hydrological model, a noval ensemble forcasting method based on the flood fuzzy classification is introduced for improving the predictive accuracy. Based on the fuzzy classification, parameters of the different flood categories are calibrated individually, then, the rainfall-runoff forecasting can be achieved using the "equifinality" parameters. First, the ensemble prediction is employed for the individual flood category, further, they are combined using the weighed averaging method. The case indicates the method proposed can improve the forecasting accuracy compared with the traditional forecast method which only use one set of parameters, and can increase reservoir discharge in advance which can effectively decrease the highest water level and flood control risk in the big flood regulation.
Finally, a summary is given and some problems to be further studied are discussed.
(1)针对以往中长期径流预报通常仅采用单一方法进行建模与预报,难以利用各预报方法优势的缺陷,本文构建了一个中长期径流的年内分阶段组合预报模型。模型选用多种预报方法进行组合预报,从而发挥各方法的预报优势以达到提高预报精度的目的。针对年内不同时期径流成因的差异,将年内径流分为多个阶段,对各阶段分别建立相应的组合预报模型。特别针对径流受降雨因素影响较大的阶段,其预报不确定性增强的特点,引入贝叶斯平均(BMA)组合预报模型,可同时发布确定性预报与概率预报。通过对二滩水电站历史月径流的模拟预报,表明分段组合预报模型较单一预报方法能够提高确定性预报精度,且发布的概率预报较为可靠,可为水电站预报调度提供决策支持。
(2)为合理利用河川径流,提高水电站的水能利用率,将径流分段组合预报模型应用于水电站的中长期优化调度中,针对利用预报信息指导优化调度所带来的风险,研究了水电站中长期发电调度的模糊风险计算新方法。方法主要包括基于分段组合预报模型的年内月径流递推组合预报方法:以年发电量最大作为优化目标的水电站优化调度模型。以组合预报模型提供的不同径流描述作为水电站的预报来水,以优化调度模型求解调度决策。为量化预报误差可能对发电调度带来的风险,研究了水电站中长期调度的发电模糊风险计算新方法,较常规风险计算方法能够合理的描述风险的模糊性并量化风险损失的程度。通过对二滩水电站典型年的模拟调度,表明将分段组合预报模型应用于水电站的中长期优化调度中是可行的、有效的,模糊风险分析方法能够较常规方法更合理的描述风险从而便于决策。
(3)针对新安江模型参数优选中存在的不确定性问题,提出了模型参数的两阶段多目标优化率定方法。首先为便于分析目标权重对率定结果的影响,解决应用随机模拟方法求解该问题时造成计算量巨大的瓶颈,在第一阶段采用MOSCEM-UA算法进行参数优选,以此作为分析对象,从而有效缩减了求解问题的规模和计算量,在第二阶段通过分析目标权重对模型参数优选的影响,建立了基于排序可靠度特征值的满意参数优选方法。针对目标权重的确定,将数学与经验权重进行组合确定目标组合权重,同时分析了不同的数学权重计算方法与不同优选目标偏好对参数优选结果的影响。实例分析表明,该方法能够在综合评价参数率定目标特征值与排序可靠度的基础上选择折衷满意模型参数,并能够提供相应的排序不确定性信息。
(4)对新安江模型的参数与输入不确定性进行了研究。以往研究多集中于对模型参数的不确定性分析上,而对模型输入的不确定性研究较少,因此,以模型参数与模型输入不确定性作为研究对象,采用SCEM-UA方法对场次洪水模拟不确定性进行了分析。与普遍采用的GLUE方法相比,SCEM-UA方法无需主观定义似然函数临界值,采用算法收敛后的非劣参数作为可行参数。通过对桓仁水库场次洪水模拟的不确定性分析,表明模型参数与输入对洪水模拟结果有显著的影响,且后者的影响较大,因此,在实时洪水预报时应对此予以充分考虑以量化预报风险。
(5)针对洪水预报模型存在的“异参同效”问题,为提高模型的预报精度与可靠度,研究了模糊分类洪水组合预报方法。该方法在对洪水进行模糊分类的基础上,对各类洪水分别进行预报模型参数率定,从而提高了场次洪水模拟精度。通过利用同类洪水中具有“同效”现象的多组参数进行洪水预报,采用相应的组合方法进行分类内预报结果组合,进而再对各分类的预报结果进行模糊加权组合得到洪水组合预报值。实例应用表明,该方法能够提高场次洪水预报精度。将模糊分类洪水组合预报方法应用于实时洪水预报调度中,与采用一组满意参数进行洪水预报方法相比,在一些大量级场次洪水中可使水库提前加大泄流量从而有效降低调洪最高水位并减小水库的防洪风险。
最后对全文做了总结,并对有待于进一步研究的问题进行了展望。
With the aggravation of the water resources scarcity and frequently flood disaster, it's urgent to take certain measures for utilizing limited water resources and improving the efficiency of the hydropower with the current hydraulic engineering under the safety of flood control. Hydrological prediction and optimal operation is one of the effective solutions for flood protection and can improve the benefit for hydropower station. Therefore, based on the Huanren hydropower station and Ertan hydropower station, some key problems for the runoff prediction and optimal operation of the hydropower station are studied. In this dissertation, firstly, the ensemble prediction model by stages (EPMS) is developed for medium-long term hydrological prediction, the model developed utilizes the advantages of various traditional and intelligent methods for runoff prediction, and then, based on the different runoff description, a new method for hydropower station optimal operation and risk analysis is presented. As for the flood forecasting, much attention have been put on the uncertainty of the hydrological model, therefore, take the Xinanjiang model as an example, this dissertation studies the uncertainty resulting from the optimal selection, model parameters calibration and model input error. Further, the method for flood ensemble forecasting based on the fuzzy classification is developed. The major research work is outlined as follows:
(1) The ensemble prediction model by stages is presented for medium-long term hydrological prediction to overcome the disadvantages of the single method. The model developed using a number of models for streamflow forecasting so as to improving the forecasting accuracy. Considering the differences of the runoff formation, the year is divided into several periods, then, the ensemble model for each periods is established. Due to the uncertainty of the precipitation, the Bayesian Model Averaging (BMA) is employed as the ensemble model for certain period, which can release both deterministic and probabilistic forecast. Take the Ertan hydropower station as the study case, results indicate the model proposed is superior to the single forecasting model and the probabilistic prediction is reliable which can support decision making.
(2) To impove the waterpower utilization ratio, EPMS is employed in the medium-long term optimal operation for hydropower station. For quantifing the power generation risk due to the flow prediction error, a noval method called fuzzy risk analysis is developed. The method proposed includes: monthly streamflow recursive prediction using the EPMS; Optimal operation model with the object of maximum the annual power generation, which uses the prediction of different runoff description provided by EPMS; Fuzzy risk analysis method for the power generation. The method is applied to the Ertan hydropower station located in the Yalong river, the operation results for the typical years indicate the effectiveness and efficiency of the model, and the fuzzy risk analysis method can describe the risk more reasonabily than commom method.
(3) Aiming at the uncertainty of the Xinanjiang hydrological model parameters selection, a noval two-stage parameters calibration method is presented. In the first stage, in order to analyzed the sensitivity of the objective weights which will lead to bottleneck when employs the stochastic simulation method, the MultiObjective Shuffled Complex Evolution Metropolis (MOSCEM-UA) algorithm is employed for searching the non-dominated solutions, thus the computational complexity can be reduced effectively, further, the satisfactory solution is determined using the sorting characteristic value considering the ranking reliability in the second stage. Furthermore, the influence of using different methods for calculating the mathematical weights and combination weights on the alternatives ranking is investigated. The study case demonstrates the proposed method can provide both satisfactory parameters and ranking reliability.
(4) Predictive uncertainty analysis aiming at the parameters and input of the Xinanjiang model is presented. In this study, the predictive uncertainty analysis considering the model parameters and model input using the Shuffled Complex Evolution Metropolis (SCEM-UA) algorithm is proposed. Compared with the GLUE method, SCEM-UA algorithm doesn't require determine the value of the cut-off threshold for dividing the behavior and non-behavior parameter set, it only using the non-inferior parameters for flood simulation based on the appropriate converage of the algorithm. Taking the Huanren reservoir as an example, shows the model parameters and model input have the significant influence on the rainfall-runoff simulation, and the latter is larger than the former. Therefore, the predictive uncertainty should be fully considered during the real time flood forecasting.
(5) Aiming at the equifinality of the hydrological model, a noval ensemble forcasting method based on the flood fuzzy classification is introduced for improving the predictive accuracy. Based on the fuzzy classification, parameters of the different flood categories are calibrated individually, then, the rainfall-runoff forecasting can be achieved using the "equifinality" parameters. First, the ensemble prediction is employed for the individual flood category, further, they are combined using the weighed averaging method. The case indicates the method proposed can improve the forecasting accuracy compared with the traditional forecast method which only use one set of parameters, and can increase reservoir discharge in advance which can effectively decrease the highest water level and flood control risk in the big flood regulation.
Finally, a summary is given and some problems to be further studied are discussed.
引文
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