基于粗糙集的支持向量机地下水质量评价模型

以辽宁绥中县第四系松散岩类孔隙水的10组水质监测数据为基础,选取pH值、Cl^-、SO₄^2-、NH₄⁺、NO₂^-、NO₃^-、F^-、总硬度、总溶解固体等14项水质评价指标,采用粗糙集对指标进行约简,将基于属性依赖度和信息熵的启发式算法结合,获得属性约简集,应用支持向量机分别评价约简前后的地下水质量.结果表明,属性约简将14项水质指标精简为8项,水质评价结果与约简前保持一致,区域地下水普遍在III类以上,部分地区铁、"三氮"等超标,不适宜饮用.粗糙集和支持向量机的联合应用,在保证分类能力的前提下有效地减少冗余指标,降低运算维度,保证水质评价的合理性.

Model of groundwater quality assessment with support vector machine based on rough set

A total of 10 quaternary loose rock pore water samples were collected from Suizhong County, Liaoning. The pH, Cl^-, SO₄^2-, NH₄⁺, NO₂^-, NO₃^-, F^-, total hardness, total dissolved solids, iron, manganese, zinc, cyanide and volatile phenols were considered as the water quality parameters. Rough set theory was employed for data reduction. Meanwhile, to find attribute reduction set, the attribute dependence degree and information entropy heuristic algorithms were combined. Support vector machine was employed to evaluate groundwater quality for all parameters before and after reduction, respectively. The results showed that rough set theory reduced the number of chemical parameters from 14 to 8, and assessment results with attribute reduction were the same as those without attribute reduction. The groundwater quality in the study area was mainly class II and III, which meets the permissible limits. However, iron and three nitrogen were exceeded drinking water quality standard. Although the combination of rough set and support vector machine reduced redundant indices, the accuracy of water quality classification remained effective, while the complexity of calculation was reduced and the rationality of assessment results was guaranteed.