植物滞留系统智能优化水量水质与适应性调控

The optimization of bioretention systems is important for the urban water quality improvement and rainwater management. There are defects for lacking intelligence and dynamic characteristics in the existing models and methods because of its static state, which can not eliminate many uncertainties. And it is not clear for the optimization and regulation mechanism on water quality and water quantity under bioretention systems. To scientifically describe and explain the optimization processes and regulation mechanism of water quantity and water quality for the bioretention systems and reduce the uncetainties, we will establish water quantity-water quality intelligent optimization and adaptive regulation model for bioretention systems through the grey-code accelerating evolution, adaptive analysis, set pair analysis and intelligent calculation. The processes of optimization and adaptive regulation for water quality and quantity in bioretention systems will be quantitatively described. Theory of the intelligent optimization and adaptive regulation of the water quantity and water quality of bioretention system are set up based on set pair analysis and calculation intelligent methods. The theroy and models will be applied in the real urban areas, in which the construction optimization and regulation management of the water quality and quantity for bioretention systems will be analyzed, and the optimal structure,distribution and adaptive regulation models of the water quality and quantity for bioretention systems will be designed. This study will provide a new idea for the optimization and regulation of the water quantity and water quality for bioretention systems which have important scientific significance and application value for the urban water environment protection, rainwater management, and eco-civilization construction. And it can also effectively promote the theory interdisciplinary, innovation and development for hydrology, ecology, environmental science and engineering, and computational intelligence.

优化植物滞留系统对城市水系水质改善和雨洪管理具有重要意义。现有的方法与模型，以静态为主，缺乏智能性与动态性，难以解决植物滞留系统结构优化调控中的多种不确定性，植物滞留系统优化调控水量水质机理不清。本研究采用申请人提出的格雷码加速进化方法，并与集对分析、适应性分析等方法综合集成，建立植物滞留系统水量水质智能优化模型与适应性调控模型，定量描述水量水质优化与适应性调控过程，提出植物滞留系统水量水质智能优化与适应性调控的不确定性智能集对分析理论。并将所建理论方法应用于典型城市研究区，阐明植物滞留系统优化水量水质与适应性调控机理，优化系统结构与分布，构建适应性调控模式。研究将为植物滞留系统优化水量水质与调控提供全新的思路，不仅对城市水环境保护、雨洪管理和生态文明建设等具有重要的科学意义和应用价值，而且又可有力促进水文学、生态学、环境科学和工程与计算智能等学科理论的交叉、创新和发展。

本项目从植物滞留系统（BRS）水量模拟与智能优化、BRS水量水质智能优化模拟、BRS适应性调控等方面开展“植物滞留系统智能优化水量水质与适应性调控”研究，提出了植物滞留系统水量水质智能优化与适应性调控理论方法，建立了植物滞留系统水量水质模拟的系列智能优化模型、智能集对分析评价模型和适应性调控模型，并将所建立的理论模型应用于北京市植物滞留系统水量水质智能优化与适应性调控。已发表论文41篇，其中期刊论文33篇（SCI论文23篇），会议论文8篇；获软件著作权2项。共培养研究生11名。重要贡献如下：.1）在BRS水量模拟与智能优化方面，建立了马尔科夫链降雨量预测模型、提出并构建了一个简单的适用于短历时、强降雨的BRS水量模拟解析下渗模型。建立了BRS水量优化模拟的分数阶遗传算法和混沌格雷码加速遗传算法等智能优化模型。并将所建立的模型应用于北京典型地区，提高了BRS水量模拟的精度。本研究为BRS智能优化模拟提供了理论基础。.2）在BRS水量水质智能优化模拟方面，构建了用于BRS优化调控的基于格雷码加速遗传层次分析的模糊Logistic集对分析模型和基于熵权的TOPSIS模型。并结合SWMM模型对区域BRS水量水质进行智能优化模拟，并将所建立的模型应用于北京典型地区。对区域径流总量、径流峰值、径流峰值出现时刻、综合径流系数、多种污染物（Cu、Zn、TN、TP）总量、峰值等模拟结果表明，BRS最优措施方案与基础情景相比，当遇到小于10年一遇的降雨时，BRS措施对水质水量表现出了明显的削减能力。本研究阐明了BRS水量水质智能优化模拟的调控机制。.3）在BRS适应性调控方面，针对系统的动态性和不确定性，引入碳足迹值，构建了用于BRS水质水量与生态综合调控的层次分析与集对分析模型；引入WinSLAMM模型，对BRS水质水量进行适应性调控。并将所建理论方法与模型应用于北京典型地区，并给出了植物滞留系统尺寸、数量、总覆盖面积最佳调控模式。本研究丰富发展了植物滞留系统适应性调控理论方法。.