基于数据挖掘和关联分析的环境质量评价模型构建与仿真

Meimei Wang, Duoyong Zhang, Huimei Xu
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引用次数: 0

摘要

本文对基于数据挖掘和关联分析的环境质量评价模型进行了研究。随着多元统计分析方法的应用,大数据分析法在环境质量评价中得到了应用。该方法从多个目标之间的互易性开始变为多个不相关的总体目标之间的互易性,优点在于考虑了各个目标之间的相关性,能够最大限度地保留原始信息,对高维数据进行最佳的综合降维处理。在此基础上,提出了基于数据挖掘和关联分析的模型。分析灰色关联的基本任务是基于因素序列的微观或宏观几何关系是否接近,分析决定因素之间的因素对主要行为的影响程度和贡献程度,而灰色关联空间是分析灰色关联的基础。将神经网络和灰色分析相结合的方法应用于空气和水的质量评价。实验结果反映了该模型的有效性,能有效地评价环境质量。
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Construction and Simulation on Environmental Quality Evaluation Model Based on Data Mining and Correlation Analysis
In this paper, we conduct research on environmental quality evaluation model based on data mining and correlation analysis. Along with the application of multi-statistical analysis method, the big data analysis law by has been applied in the environmental quality evaluation. Reciprocities of this method among from many targets starts that changes into a few not related overall targets many targets and the merit lies in had considered the relevance among various targets that can maximum limit retain original information, carries on best comprehensive dimensionality reduction processing to the high dimensional data. Aside by using this feature, this paper proposes the data mining and correlation analysis based model. The basic task of the analytical grey incidence is the microscopic or macroscopic geometry of behavior based factor sequence is close, to analyze and contribution degree of influence or the factor between determination factors to main behavior, but the gray incidence space carries on the foundation of analytical grey incidence. We implement the model on the air and water quality evaluation which are assisted with the neural network and gray analysis. The experimental result reflect the effectiveness of our model, it can evaluate the environmental quality effectively.
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