【作者】 武君；

【导师】 汪家权；

【作者基本信息】 合肥工业大学 ， 环境工程， 2005， 硕士

【摘要】 水质模拟预测是顺利实现水环境规划管理、水污染综合防治等任务不可缺少的基础工作,是具有普遍意义的一项重要内容。机理性水质模型虽考虑了影响水质变化的诸多因素,模拟预测效果较理想,但往往较复杂并需要大量基础资料与数据,这常使其在我国许多河流系统中的进一步应用受到限制。而非机理性水质模拟因其针对某一特定的水质系统,通过数学统计或其他数学方法建立模型,也常可以取得较好的模拟预测效果。 本文以淮河安徽段水质为研究对象,结合国家自然科学基金“河流水质虚拟调控—以淮河安徽段为例(50379003)”项目,在查阅文献、总结机理性水质模型基础上,以非机理性方法在水质模拟预测中的应用为主要内容,运用具体实例探讨了非机理性常用方法和新方法的应用。 在常用的非机理性方法中,论文结合具体的实例,分别采用了加权和非加权式马尔可夫法、自回归时间序列模型对水质指标进行建模预测,并对灰色模型进行改进,建立了灰色时序组合模型。通过实例发现:这些常用非机理性方法在水质模拟预测工作中具有一定使用价值。 在非机理性新方法应用中,论文采用BP神经网络法对淮河安徽段水质进行了模拟预测,训练数据模拟效果很好,预测检验的结果也都在可以接受的范围内。论文将混沌理论和分形理论应用于水质模拟预测中,提出了混沌全域法及其降维改进法、分段变维分形法、分形插值等方法并结合实例探讨了其对水质模拟预测的可行性,为今后工作提供了新的思路和方法。更多还原

【Abstract】 It is important and basic to simulate and predict the water quality for successfully accomplishing the tasks about water environment. The mechanism water quality models take into account the factors that have impact on change of water quality, so the simulating and predicting results are usually satisfactory. But they often require a great deal of background information, which makes them have some limits to application for many river systems. However, water quality models without considering mechanism often acquire satisfactory simulating and predicting results because they built models aiming at specific water quality system by using statistical method or other mathematical methods.The paper regards Huaihe River in Anhui Province as the subject by participating in the program of National Natural Science Fund River Water Quality’s Virtual Regulation& Control Illustrated with Huaihe River in Anhui Province (50379003). By consulting the documents and summarizing mechanism water quality models, the paper discusses the application of classical methods and innovatory methods without considering mechanism for simulating and predicting water quality with the examples.As for classical methods without considering mechanism, the paper adopts Markov method of weighting, Markov method without weighting, and Auto Regressive time series method to simulate and predict water quality items, responsively. It also improves the traditional grey model by using combined model. The results of examples illustrate that these classical methods have some use value for simulating and predicting water quality.As for innovatory methods without considering mechanism, the paper adopts BP Artificial Neural Network. The simulating results of training data are satisfactory, and predicting results are acceptable. The paper tries to simulate and predict water quality by using chaos theory and fractal theory. The paper brings forward the idea of universe chaos prediction and its improvement by reducing dimension, method of sectioned variable dimension fractal, and fractal interpolation. Using examples, it discusses the feasibility of these methods to simulate and predict water quality. Therefore, the paper will provide new ideas and methods for the following tasks.更多还原