水资源利用区域差异分析及综合管理模型研究

发布时间：2018-01-28 09:49

本文关键词： 用水强度水资源利用 IPAT恒等式指标分解混沌综合水资源管理综合水资源管理动力系统综合水资源管理混沌吸引子　出处：《江苏大学》2017年硕士论文　论文类型：学位论文

【摘要】：在自然原因导致中国水资源分布不均匀、社会原因导致用水量增加和水质量下降的情况下,降低用水强度和强化水资源综合管理对有效利用中国水资源具有重要意义。本文分析中国用水强度的区域差异,并在此基础上建立综合水资源管理动力系统模型。主要内容如下:(1)中国用水强度的区域差异。扩展IPAT恒等式到水资源领域,将用水强度分解成七个影响因子:农业用水、工业用水、生活用水、生态用水、水资源开发利用率、人均水资源和人口强度。借助主成分分析法将该七个影响因子缩减为四个包含原有大量信息且互不相关的主成分,即农业用水成分、生态用水成分、供水能力成分和水资源承载能力成分,进而得到综合用水强度指标。以四个主成分和综合用水强度作为变量进行聚类分析。采用F-统计量确定最优聚类数为3。对k-均值聚类、模糊c-均值聚类和高斯混合模型三种聚类方法进行比较,从而选用最优的k-均值聚类作为中国用水强度区域差异的最终结果,得到三种中国的区域用水强度模式:第一种模式包含10个省份,主要位于中国的西北部,属于中国经济欠发达地区,用水强度最大;第二种模式包含16个省份,主要位于中国东南部,属于中国经济发达或者中等发达地区,用水强度大小中等;第三种模式包含5个省份,主要位于中国东部,属于经济相对发达的地区,用水强度最小。进一步分析中国区域用水差异的原因和对中国生态文明建设的启示。(2)综合水资源管理系统的动力学分析。基于风险因子、管理因子和地区总用水量三个变量的复杂关系,建立综合水资源管理动力系统,获得综合水资源管理混沌吸引子。通过Lyapunov指数谱、分岔图和Poincar′e映射研究系统的混沌动力学特征,数值分析展示了系统大量的混沌动力学行为。通过对管理因子的线性反馈控制将系统稳定到一个极限环。从而得出加大政府对水资源的控制力度有利于水资源利用管理的结论。
[Abstract]:Natural causes lead to uneven distribution of water resources in China, and social reasons lead to an increase in water use and a decrease in water quality. Reducing the intensity of water use and strengthening the integrated management of water resources are of great significance to the effective utilization of water resources in China. This paper analyzes the regional differences of water use intensity in China. On this basis, the dynamic system model of integrated water resources management is established. The main contents are as follows: 1) the regional difference of water intensity in China. The IPAT identity is extended to the field of water resources. The water intensity was decomposed into seven factors: agricultural water, industrial water, domestic water, ecological water, and utilization of water resources. With the aid of principal component analysis, the seven influencing factors were reduced to four principal components which contained a large amount of information and were not related to each other, that is, agricultural water use composition and ecological water use component. Water supply capacity components and water resources carrying capacity components. Then the comprehensive water use intensity index was obtained. The four principal components and the comprehensive water use intensity were used as variables to cluster analysis. Using F- statistics to determine the optimal clustering number was 3. The fuzzy c-means clustering and Gao Si mixed model were compared, and the optimal K-means clustering was selected as the final result of the regional difference of water intensity in China. Three regional water intensity models of China are obtained: the first model consists of 10 provinces, mainly located in the northwest of China, which belongs to the underdeveloped regions of China and has the highest intensity of water use; The second model consists of 16 provinces, mainly located in the southeast of China, which belongs to the economically developed or moderately developed areas of China, and the intensity of water use is moderate. The third model consists of five provinces, mainly located in eastern China, belong to the relatively developed region. Water use intensity is the smallest. Further analysis of the reasons of regional water use difference in China and the inspiration to the construction of ecological civilization in China. 2) dynamic analysis of integrated water resources management system based on risk factors. Based on the complex relationship between the three variables of management factor and regional total water consumption, a dynamic system of integrated water resources management is established, and chaotic attractor of integrated water resources management is obtained. The Lyapunov exponent spectrum is adopted. The chaotic dynamics of the system is studied by bifurcation diagram and Poincar'e map. Numerical analysis shows a large number of chaotic dynamic behaviors of the system. The system is stabilized to a limit cycle by linear feedback control of management factors. It is concluded that increasing government control of water resources is beneficial to water resources. Use management conclusions.
【学位授予单位】：江苏大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TV213

【参考文献】