基于神经—模糊理论的冰情预报研究
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摘要
河冰是北方河流和水利工程的重要研究课题。近些年冰情灾害发生范围有所扩大,给经济发展带来的危害和损失在不断增加,防治冰凌灾害已经成为国民经济发展中必须重视的问题。防治冰凌灾害的首要任务是加强冰情预报的研究,建立健全冰情预报系统。所以本论文开展了模糊—神经理论预报明渠冰情的研究。
本文主要创新点如下：
(1)研发了基于Levenberg-Marquart算法改进BP神经网络(ANN)的冰情预报系统。建立了基于Levenberg-Marquart算法改进的Back Propagation(BP)神经网络模型,并把该模型应用到黄河宁蒙河段实时冰情预报中。通过比较,神经网络模型的预报精度明显高于智能的遗传算法(G(0,N))和传统的统计模型。该模型已经投入到黄河宁蒙河段冰情运行管理中,连续多年在线成功的预报了黄河宁蒙河段流凌、封河和开河日期,为黄委会黄河凌汛提前决策提供了重要的科学依据。
(2)提出了自适应神经模糊推理系统(ANFIS)预报河道冰情。鉴于自适应网络的模糊推理系统吸收了神经网络和模糊理论两者优点,本论文提出用ANFIS预报河渠冰情,模拟了黄河宁蒙河段石嘴山、巴彦高勒、三湖河口、头道拐水文站冬季水温的变化。同时把ANN结果同ANFIS预报结果进行比较,证明(?)NFIS模型比神经网络理论具有更高的预报精度。
(3)引用中国传统二十四节气到冰情预报研究中。把中国传统二十四节气应用到冰情预报中,开展南水北调中线工程冬季输水冰情预报。把立冬日作为预报的时间基准点,采用Levenberg-Marquart算法改进的BP神经网络模型预报南水北调中线黄河以北6个城市的气温稳定转正和转负日期。并同公历作为时间坐标系预报结果进行对比,前者预报精度好于后者。在预报因子选取中也考虑到农历二十四节气作为时间节点的参考价值。
The ice conditions are natural phenomena appeared in the rivers of cold regions, which have important impacts on hydraulic engineering. The disaster of the ice condition has increased in both the degree and range in the recent years, which damage to the economic and social development is more serious. Therefore, accurate forecast of the ice conditions is very important for against the potential disaster. Therefore, the Neuro-Fuzzy theory is applied to forecast ice conditon in this study,. The innovations are are follows.
(1) An the artificial neural networks (ANN) model based on feed-forward back-propagation (FFBP) and improved by Levenberg-Marquardt (L-M) algorithm is developed to forecast the ice condition. Because of the complexity of ice conditions, traditional methods could hardly give accurate prediction in the ice condition forecast, while ANN have obvious advantage over other traditional methods for forecasting ice condition. An ANN model based on BP and improved by L-M algorithm is developed to forecast the ice condition including ice run date, freeze-up date, break-up date and so on in the Inner Mongolia Region of the Yellow River. The forecast results are in good agreement with the measured ones. Simulation also shows that the ANN model is superior to the MLR model and GM(0,1) model. This forecast system has been runing for the several years to provide the reliable information of Yellow River in the winter management.
(2) Adaptive-Network-based Fuzzy Inference System(ANFIS) is applied to ice condition. With its hybrid learning scheme, ANFIS, constructed under the framework of the neural networks and fuzzy models, and the latter possess certain advantages over the former two, is convenient for modelling the nonlinear multivariable process. Consequently, ANFIS is applied to the freeze-up water temperature forecast in Bayangaole, Shanhuhekou, Shizuishan and Toudaoguai Hydrometric Station of Inner Mongolia reach of the Yellow River. Through such comparisons, it was discovered that the water temperature forecast results approximately agree with those of the field records. Compared with the results of ANFIS and ANN, ANFIS model was founds to be superior to ANN model for forecasting the time series information.
(3) Chinese Calender is introduced to forecast the ice condition. In this study, the essence and characteristic are analyzed theoretically for Lunar Calendar, Solar Calendar and Chinese Calender. Though reasearch on the relationship between the Chinese Calender and modern hydrology, it is reasonable and scientific to apply the coordinate system of Chinese Calender to forecast hydrological information. Chinese Calender is associated with the ANN to forecast ice condition in South-to-North Water Diversion Middle Route Project. It is difficult to forecast ice condition because there are no existing hydrological data for this building Project. By analyzing the meteorological conditions, BP-ANNs model improved by L-M algorithm is applied to forecast the date of temperature downcrossing0℃and the date of temperature upcrossing0℃in the coordinate system of Chinese Calender and Solar Calendar, respectively. Obviously, the accuracy is more improved when the Beginning of Winter is as the datum points of the statistic date in the coordinate system of Chinese Calender than that in Solar Calendar. This result show that the Twenty-four Solar Terms is as an important reference for the hydrological forecast correlated the weather and Climate.
本文主要创新点如下：
(1)研发了基于Levenberg-Marquart算法改进BP神经网络(ANN)的冰情预报系统。建立了基于Levenberg-Marquart算法改进的Back Propagation(BP)神经网络模型,并把该模型应用到黄河宁蒙河段实时冰情预报中。通过比较,神经网络模型的预报精度明显高于智能的遗传算法(G(0,N))和传统的统计模型。该模型已经投入到黄河宁蒙河段冰情运行管理中,连续多年在线成功的预报了黄河宁蒙河段流凌、封河和开河日期,为黄委会黄河凌汛提前决策提供了重要的科学依据。
(2)提出了自适应神经模糊推理系统(ANFIS)预报河道冰情。鉴于自适应网络的模糊推理系统吸收了神经网络和模糊理论两者优点,本论文提出用ANFIS预报河渠冰情,模拟了黄河宁蒙河段石嘴山、巴彦高勒、三湖河口、头道拐水文站冬季水温的变化。同时把ANN结果同ANFIS预报结果进行比较,证明(?)NFIS模型比神经网络理论具有更高的预报精度。
(3)引用中国传统二十四节气到冰情预报研究中。把中国传统二十四节气应用到冰情预报中,开展南水北调中线工程冬季输水冰情预报。把立冬日作为预报的时间基准点,采用Levenberg-Marquart算法改进的BP神经网络模型预报南水北调中线黄河以北6个城市的气温稳定转正和转负日期。并同公历作为时间坐标系预报结果进行对比,前者预报精度好于后者。在预报因子选取中也考虑到农历二十四节气作为时间节点的参考价值。
The ice conditions are natural phenomena appeared in the rivers of cold regions, which have important impacts on hydraulic engineering. The disaster of the ice condition has increased in both the degree and range in the recent years, which damage to the economic and social development is more serious. Therefore, accurate forecast of the ice conditions is very important for against the potential disaster. Therefore, the Neuro-Fuzzy theory is applied to forecast ice conditon in this study,. The innovations are are follows.
(1) An the artificial neural networks (ANN) model based on feed-forward back-propagation (FFBP) and improved by Levenberg-Marquardt (L-M) algorithm is developed to forecast the ice condition. Because of the complexity of ice conditions, traditional methods could hardly give accurate prediction in the ice condition forecast, while ANN have obvious advantage over other traditional methods for forecasting ice condition. An ANN model based on BP and improved by L-M algorithm is developed to forecast the ice condition including ice run date, freeze-up date, break-up date and so on in the Inner Mongolia Region of the Yellow River. The forecast results are in good agreement with the measured ones. Simulation also shows that the ANN model is superior to the MLR model and GM(0,1) model. This forecast system has been runing for the several years to provide the reliable information of Yellow River in the winter management.
(2) Adaptive-Network-based Fuzzy Inference System(ANFIS) is applied to ice condition. With its hybrid learning scheme, ANFIS, constructed under the framework of the neural networks and fuzzy models, and the latter possess certain advantages over the former two, is convenient for modelling the nonlinear multivariable process. Consequently, ANFIS is applied to the freeze-up water temperature forecast in Bayangaole, Shanhuhekou, Shizuishan and Toudaoguai Hydrometric Station of Inner Mongolia reach of the Yellow River. Through such comparisons, it was discovered that the water temperature forecast results approximately agree with those of the field records. Compared with the results of ANFIS and ANN, ANFIS model was founds to be superior to ANN model for forecasting the time series information.
(3) Chinese Calender is introduced to forecast the ice condition. In this study, the essence and characteristic are analyzed theoretically for Lunar Calendar, Solar Calendar and Chinese Calender. Though reasearch on the relationship between the Chinese Calender and modern hydrology, it is reasonable and scientific to apply the coordinate system of Chinese Calender to forecast hydrological information. Chinese Calender is associated with the ANN to forecast ice condition in South-to-North Water Diversion Middle Route Project. It is difficult to forecast ice condition because there are no existing hydrological data for this building Project. By analyzing the meteorological conditions, BP-ANNs model improved by L-M algorithm is applied to forecast the date of temperature downcrossing0℃and the date of temperature upcrossing0℃in the coordinate system of Chinese Calender and Solar Calendar, respectively. Obviously, the accuracy is more improved when the Beginning of Winter is as the datum points of the statistic date in the coordinate system of Chinese Calender than that in Solar Calendar. This result show that the Twenty-four Solar Terms is as an important reference for the hydrological forecast correlated the weather and Climate.
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