现代洪水预报技术研究
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摘要
中国水利正由工程型向资源型转变。作为资源型水利中的重要组成部分——径流预报系统不仅是一项适应自然，减免损失非常重要的防洪非工程措施，也是一项合理利用水能、水资源的非工程措施，越来越引起世界各国的重视。正确及时的洪水预报不仅可以带来可观的经济效益，而且可以产生良好的社会效益。
水文系统在某种程度上具有开放的复杂巨系统的特征，随着水文尺度由微观到中观、宏观，水文系统的复杂性和不确定性就愈突出，表现有空间变异性、单元与系统的不一致性、模型的再参数化及不同尺度模型耦合等问题。由于研究开放的系统必须处理大量的信息和知识，这些信息和知识既包含确定性的，又包含随机性的，单一的定性解释或单一的数学模型都无法完全地处理和利用它们，宜采用从定性到定量的综合集成方法。即将复杂的分级层次结构，高层次与低层次之间相互作用通过非线性模型综合处理或者将确定性和不确定性知识分离开来，分别处理。在这方面，人工神经网络、遗传算法、小波分析以及混沌理论等新兴非线性数学技术提供了有效的处理手段。论文在分析这些新兴数学工具理论知识的基础上，结合水文现象的复杂性，力求将这些模型耦合起来研究洪水预报。
(1) BP-GA模型应用于洪水预报。论文从以下方面做了一些工作：
①确定BP网络拓扑结构。论文列出若干个前期降雨量因子，利用逐步回归算法从中挑选出影响因素大的作为网络的输入，通过“试错法”确定隐节点数。
②BP网络目标函数的选取。综合绝对误差函数和相对误差函数于一体，提出一种适合于水文研究的BP神经网络综合目标函数。
③遗传操作算子取值分析。提出8种方案来分析遗传操作算子(种群规模、交叉概率和变异概率)之间的联系，并根据精度最高原则最终确定一组合适的遗传操作算子。
(2) 小波软阈值降噪与BP神经网络综合模型(NNBP模型)应用于洪水预报。实测的水文数据中常常带有由于许多未知因素的干扰而产生的噪声，为减少噪声对BP神经网络训练过程的干扰，先用小波软阈值技术对原始径流序列进行降噪然后再进行网络训练。
(3) 小波分解高频项和低频项独立预报模型(HGCM模型)应用于洪水预报。论文首先借助小波分析，将实测径流时间序列分解为高频项和低频项两项，其次对这两项分别用混沌理论和逐步回归理论建模，其中混沌预报借助基于自组织法求解的的Volterra级数来完成，然后将两者结果叠加起来。
(4) BP-GA混合算法与混沌结合模型(BPCM)应用于洪水预报。论文证明了拟合残差Lyapunov指数为正(具有混沌特性)，并借助基于自组织法求解的Volterra滤波器对残差进行预报，将预报结果与BP-GA预报结果叠加起来。
China water conservancy is changing from engineering style to resource style. The runoff forecast system, as an important element of the resource style water conservancy , not only is a flood control non-engineering measure, but also can be applied to put water resources to rational use, which becomes more and more attractive by the world recently. The correct runoff forecast in time can yield good economic and social returns.
The hydrology system have some characters of complicated open macro system to a certain extent, the complexity and indeterminacy of the hydrology system becomes more and more standing out with the hydrology scale changing from the microscopical to the midscopical, even to the macroscopical, which present spatial variability, differ between the unit and the system, the model re-parameterization and the different scale model coupling. Since a mass of the deterministic and probabilistic information and knowledge must be handled to research the open system, and the single qualitative explanation or the single model can not deal with them entirely, a method integrating the qualitative with the quantitative should be employed. That is to say, complicated classify or hierarchical structure or the interactivity between the higher grade the lower grade should be analyzed synthetically through nonlinear model or the deterministic and probabilistic information and knowledge should be separated and then analyzed respect
ively. The newly mathematical tools such as the artificial neural networks, the gene algorithm, the wavelet analysis and chaos theory have provided effective means. To increase forecast precision and length, this paper couple those theories to research the nonlinear hydrological problems.
(1) BP-GA model application to the flood forecast. In this paper, the following about this has been done:
㏕he final BP networks topological structure. Some preceding rain factors were list, then stepwise regression algorithm was employed to select the obvious factors from the list as the input of the BP networks. And the trial-and-error method is employed to define the number of the hidden layer nodes.
(2)Defined the objective function. Combining absolute error function with relative error function, and setting it as the objective function of BP networks application to the flood forecast.
(3)The span analysis of the genetic operators.8 schemes were provided to analyze the relation of the genetic operators such as population size, cross probability, mutation probability. At last, a optimum group of genetic operators was selected.
(2) The model(NNBP) combined the wavelet soft-threshold de-noising with BP-GA model application to the flood forecast. The noise were often attached to the real hydrology time series yield by some unknown factors. To reduce the influence of them to the BP networks, the wavelet soft-threshold de-noising method was employed before BP networks began to train.
(3) The collective model(HGCM)based on the individual forecast model of the high frequency and the low frequency application to the flood forecast. The real runoff time series was divided into the high frequency item and the low frequency item with the help of the wavelet analysis first, then the two items were modeled by chaos theory and the stepwise regression algorithm, at last the output of the two models were added together.
. (4) The model (BPCM) combined the BP-GA algorithm and the chaos theory application to the flood forecast. A positive Lyapunov exponent was got to show the residual series was chaotic in this paper. The volterra filter based on the self-organizing method was employed to forecast, at last the output of the two models were added together.
水文系统在某种程度上具有开放的复杂巨系统的特征，随着水文尺度由微观到中观、宏观，水文系统的复杂性和不确定性就愈突出，表现有空间变异性、单元与系统的不一致性、模型的再参数化及不同尺度模型耦合等问题。由于研究开放的系统必须处理大量的信息和知识，这些信息和知识既包含确定性的，又包含随机性的，单一的定性解释或单一的数学模型都无法完全地处理和利用它们，宜采用从定性到定量的综合集成方法。即将复杂的分级层次结构，高层次与低层次之间相互作用通过非线性模型综合处理或者将确定性和不确定性知识分离开来，分别处理。在这方面，人工神经网络、遗传算法、小波分析以及混沌理论等新兴非线性数学技术提供了有效的处理手段。论文在分析这些新兴数学工具理论知识的基础上，结合水文现象的复杂性，力求将这些模型耦合起来研究洪水预报。
(1) BP-GA模型应用于洪水预报。论文从以下方面做了一些工作：
①确定BP网络拓扑结构。论文列出若干个前期降雨量因子，利用逐步回归算法从中挑选出影响因素大的作为网络的输入，通过“试错法”确定隐节点数。
②BP网络目标函数的选取。综合绝对误差函数和相对误差函数于一体，提出一种适合于水文研究的BP神经网络综合目标函数。
③遗传操作算子取值分析。提出8种方案来分析遗传操作算子(种群规模、交叉概率和变异概率)之间的联系，并根据精度最高原则最终确定一组合适的遗传操作算子。
(2) 小波软阈值降噪与BP神经网络综合模型(NNBP模型)应用于洪水预报。实测的水文数据中常常带有由于许多未知因素的干扰而产生的噪声，为减少噪声对BP神经网络训练过程的干扰，先用小波软阈值技术对原始径流序列进行降噪然后再进行网络训练。
(3) 小波分解高频项和低频项独立预报模型(HGCM模型)应用于洪水预报。论文首先借助小波分析，将实测径流时间序列分解为高频项和低频项两项，其次对这两项分别用混沌理论和逐步回归理论建模，其中混沌预报借助基于自组织法求解的的Volterra级数来完成，然后将两者结果叠加起来。
(4) BP-GA混合算法与混沌结合模型(BPCM)应用于洪水预报。论文证明了拟合残差Lyapunov指数为正(具有混沌特性)，并借助基于自组织法求解的Volterra滤波器对残差进行预报，将预报结果与BP-GA预报结果叠加起来。
China water conservancy is changing from engineering style to resource style. The runoff forecast system, as an important element of the resource style water conservancy , not only is a flood control non-engineering measure, but also can be applied to put water resources to rational use, which becomes more and more attractive by the world recently. The correct runoff forecast in time can yield good economic and social returns.
The hydrology system have some characters of complicated open macro system to a certain extent, the complexity and indeterminacy of the hydrology system becomes more and more standing out with the hydrology scale changing from the microscopical to the midscopical, even to the macroscopical, which present spatial variability, differ between the unit and the system, the model re-parameterization and the different scale model coupling. Since a mass of the deterministic and probabilistic information and knowledge must be handled to research the open system, and the single qualitative explanation or the single model can not deal with them entirely, a method integrating the qualitative with the quantitative should be employed. That is to say, complicated classify or hierarchical structure or the interactivity between the higher grade the lower grade should be analyzed synthetically through nonlinear model or the deterministic and probabilistic information and knowledge should be separated and then analyzed respect
ively. The newly mathematical tools such as the artificial neural networks, the gene algorithm, the wavelet analysis and chaos theory have provided effective means. To increase forecast precision and length, this paper couple those theories to research the nonlinear hydrological problems.
(1) BP-GA model application to the flood forecast. In this paper, the following about this has been done:
㏕he final BP networks topological structure. Some preceding rain factors were list, then stepwise regression algorithm was employed to select the obvious factors from the list as the input of the BP networks. And the trial-and-error method is employed to define the number of the hidden layer nodes.
(2)Defined the objective function. Combining absolute error function with relative error function, and setting it as the objective function of BP networks application to the flood forecast.
(3)The span analysis of the genetic operators.8 schemes were provided to analyze the relation of the genetic operators such as population size, cross probability, mutation probability. At last, a optimum group of genetic operators was selected.
(2) The model(NNBP) combined the wavelet soft-threshold de-noising with BP-GA model application to the flood forecast. The noise were often attached to the real hydrology time series yield by some unknown factors. To reduce the influence of them to the BP networks, the wavelet soft-threshold de-noising method was employed before BP networks began to train.
(3) The collective model(HGCM)based on the individual forecast model of the high frequency and the low frequency application to the flood forecast. The real runoff time series was divided into the high frequency item and the low frequency item with the help of the wavelet analysis first, then the two items were modeled by chaos theory and the stepwise regression algorithm, at last the output of the two models were added together.
. (4) The model (BPCM) combined the BP-GA algorithm and the chaos theory application to the flood forecast. A positive Lyapunov exponent was got to show the residual series was chaotic in this paper. The volterra filter based on the self-organizing method was employed to forecast, at last the output of the two models were added together.
引文
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