摘要
在环境影响评价中,多通过建立水质模型来对地表水系统进行评价和预测,灰色系统是近年来兴起的可以用于建立水质模型的一种数学方法,本文即是研究灰色系统理论在地表水质评价及预测中的应用,并对其进行改进。
在文章中,分析了灰色系统理论研究的基本方法:系统分析法和灰色建模法。
首先,依据灰色系统分析法理论,在灰色关联度的基础上,与模糊数学理论相结合,建立了灰色模糊聚类模型,通过该模型对地表水进行评价。根据黄河兰州段新城桥断面的检测数据,应用灰色关联分析程序,分析监测数据与地表水水质标准之间的接近程度,从而确定所属水质类别,通过文章可知均为Ⅰ、Ⅱ类水质,同类水质之间的可比性较差,不利于对水体整体变化情况的把握,因此本文又进行模糊聚类分析,对同类水体进行进一步划分。根据评价结果可知该评价模型适用于地表水水质评价。
另外,对灰色建模法中的GM(1,1)预测模型的机理进行研究,发现其不足,然后通过对运算方法、步骤的改变从而建立了改进预测模型,利用深圳河十月中旬到十一月中旬一个月的数据对模型进行检验,与传统的预测模型相比,改进模型的预测精度有了很大的提高,能够很好的预测水质变化趋势。并依据2006年下半年的数据对深圳河2007年上半年的水质情况进行预测,可为深圳河管理部门建立水质变化应对方案提供科学依据。
In recent years, the seriously pollution of the surface water affects human health and the development of social economic. Therefore, we need to take some measures to control the pollution. At percent we can establish water quality model to evaluate and forecast the surface water system. The grey system is one kind of mathematical method that is used to establish the model recently. In this paper, we discuss the application of this method on the surface water quality evaluation and prediction and make some improvement on it.
In the article, we analyze the basic methods of the grey system which are system analyzing method and the method to build a grey model.
First of all, on the basis of the grey theory, we establish the grey fuzzy clustering model which matches the grey relation analysis with the fuzzy theory and use the model to evaluate the surface water. According to the inspection data about the Yellow River in Lanzhou, we use the procedure of grey correlation analysis to analyze the monitoring data and the surface water quality standards, and then determine their quality. It shows that the quality of the water belongs toⅠandⅡ. It’s difficult for us to compare the similar water quality. So in the paper we use the fuzzy clustering analysis to make further division. According to the result we can know that the evaluation model is applicable to surface water quality evaluation.
In addition, we study the mechanism of the grey GM(1,1) forecasting model and make improvement by changing the calculation type or steps. The results of the test model which bases on the data of Shenzhen River from October to November compared to the traditional model shows that the accuracy has been greatly improved and forecast can describe the water quality trends accurately. We forecast the data in the year of 2007 by the data in the year of 2006, which can be used by the management department as basis of making schema when the water quality changed.
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