【作者】 张晓伟；

【导师】 沈冰；

【作者基本信息】 西安理工大学 ， 水文学及水资源， 2009， 博士

【摘要】 水文现象的非线性和高度复杂性,要求从更高的层面和更广泛的角度应用新理论、发展新理论和提出新理论,以解决水科学研究中至今尚无法阐释和不断显现的问题。本文以水文动力系统为研究对象,针对自忆性原理在水文分析领域应用中存在的问题,应用灰色理论、多元反演理论、现代优化算法等理论和方法进行了水文时间序列的自记忆模拟与预测模型研究。研究对于挖掘预测潜力、提高预测水平具有重要理论意义和应用价值,为水文非线性模拟预测探索了一条新途径,充实了水文学的研究内容。通过研究,取得了如下主要研究成果：(1)对水文时间序列的长记忆性进行了研究。借助结构转换的思想对水文时间序列的长记忆性产生的原因进行了探讨,应用修正的R／S分析对长记忆性进行了检验,采用R／S分析统计量对记忆长度进行了确定。研究表明水文时间序列存在长记忆性,对于长记忆性产生的原因还有待于深入的研究。(2)针对灰色自记忆模型在应用中,有时产生滞后误差现象,从模型的建模机理出发,找出了模型产生滞后误差的关键因素—灰色系统动力核的背景值,通过对其改进,建立了改进的灰色自记忆模型,并应用实例说明了模型的有效性。(3)以多变量反演理论为基础,结合自忆性原理,提出了基于影响因素的水文多变量时间序列反演自记忆模式,在此基础上建立了水文多变量时间序列反演自记忆模型,通过实例研究表明,模型具有较好的适应性。(4)针对自记忆模型应用最小二乘法估计记忆系数存在的不足,结合现代优化算法,提出了基于参数优化的自记忆模型的建模原理与过程。实例研究表明,基于参数优化的自记忆模型提高了自记忆模型的适应性,具有更好的拟合、预测效果。更多还原

【Abstract】 The nonlinearity and high complexityof hydrological phenomena demands for applying new theories, developing new theories and putting forward new theories in higher level and more comprehensive point, in order to solve the problems that can’t explain and unceasingly appears until now. Hydrological dynamic system was the research objects in this study. Aiming at the problems existing in hydrology analyze, gray theory, multiple theories, and modern optimization algorithms were used to study on self-memory simulation models and prediction models of hydrological time series. This study, being of great theoretical significance and application value to develop potential and raise predicting level, provided a new way to research nonlinear simulating prediction and enriched the research contents of hydrology.The main achievements were as follows:(1) Long memory characteristics of hydrological time series was researched. The reasons of long memory characteristics of hydrological time series were discussed in virtue of structure transformation. Modified R/S analysis was used to checking long memory characteristics and the statistic was analyzed to determine the length of memory. The study showed that the hydrological time series had long memory characteristics, but the causes of long memory characteristic needed further investigation.(2) Lag errors phenomena of gray self-memory model often existed in real application. The key factors that named gray system core background value and resulted in lag error were found, according to the mechanism of modeling. New gray self-memory model was set up by modifying the background value, and the practical example showed the affectivity of the model.(3) Based on multivariable inversion theory, hydrological multivariable time series inversion self-memory mode was put forward, combined with self-memory theory, and hydrological multivariable time series inversion self-memory model was set up as well. The practical example showed that this model had good adaptability. (4) In view of the deficiency of self-memory model that adopted least squares method to estimate the memory coefficient, self-memory modeling theory and process that based on parameter optimization were put forward, combined with modern optimization algorithms. The example showed that self-memory model, based on parameter optimization, advanced the adaptability and had a better fitting effect and prediction effect.更多还原