基于支持向量机的喀斯特山区土壤环境质量评价——以贵州北部一茶叶园区为例

尚梦佳; 周忠发; 王小宇; 黄登红; 张珊珊

doi:10.11932/karst20180411

基于支持向量机的喀斯特山区土壤环境质量评价——以贵州北部一茶叶园区为例

doi: 10.11932/karst20180411

1.
贵州师范大学喀斯特研究院/地理与环境科学学院/贵州省喀斯特山地生态环境国家重点实验室培育基地
2.
贵州师范大学喀斯特研究院/地理与环境科学学院贵州省喀斯特山地生态环境国家重点实验室培育基地
3.
贵州师范大学喀斯特研究院/地理与环境科学学院/国家喀斯特石漠化防治工程技术研究中心

基金项目: 国家自然科学基金地区项目“喀斯特石漠化地区生态资产与区域贫困耦合机制研究”（41661088）；贵州省科技计划“基于北斗卫星的山地高效农业产业园区智能管理系统开发与应用”（黔科合GY字〔2015〕3001）；贵州省高层次创新型人才培养计划——“百”层次人才（黔科合平台人才〔2016〕5674）；国家遥感中心贵州分部平台建设（黔科合计Z字〔2012〕4003）（黔科合计Z字〔2013〕4003）

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出版历程
- 发布日期: 2018-08-25

Evaluation of soil environmental quality in karst mountain area based on support vector machine: A case study of a tea plantation in northern Guizhou

1.
School of Karst Science/ School of Geography & Environmental Science，Guizhou Normal University/The State Key Laboratory Incubation Base for Karst Mountain Ecology Environment of Guizhou Province
2.
School of Karst Science/ School of Geography & Environmental Science，Guizhou Normal University/The State Key Laboratory Incubation Base for Karst Mountain Ecology Environment of Guizhou Province
3.
School of Karst Science/ School of Geography & Environmental Science，Guizhou Normal University/State Engineering Technology Institute for Karst Desertification Control

摘要

摘要: 以贵州北部一茶叶园区80个表层土壤样品为研究对象，对其Hg、As、Cd、Pb、Cr和Cu含量进行测定，在MATLAB中应用支持向量机构建土壤环境质量评价模型，并与模糊综合评价法和内梅罗综合污染指数法的评价结果对比分析，探究支持向量机模型在喀斯特山区土壤环境质量评价中的适用性，其结果表明：研究区土壤质量Ⅰ类与Ⅱ类样品比例为33∶7，土壤环境质量大多数为I类；支持向量机方法的评价结果与模糊综合评价法和内梅罗综合污染指数法结果的相同率分别达到82.5%和80.0%，并分析结果有差异的样品，发现支持向量机评价结果更符合实际情况，这说明该模型适用于土壤环境质量的评价。
- 喀斯特土壤 /
- 支持向量机 /
- 环境评价
Abstract: The content of heavy metals in soil directly affects the quality and safety of tea, and even has a potential threat to human health. It is hence important to monitor, evaluate and control the content of heavy metals in the tea plantation soil. In this paper, we select a tea plantation in karst mountain area of northern Guizhou as a study area. The area is located in the transitional zone from Yun-Gui Plateau to Hunan hilly area, which belongs to the humid monsoon region of tropical plateau, with the annual precipitation of 1，000 -1，300 mm and the annual average temperature of 12.6-13.1 ℃. In this study area, because the Cambrian and Ordovician carbonate strata are widely exposed, karst landform is well developed and is characterised by interlacing occurrence of peak clusters and karst valleys. According to present situation and the characteristics of land use, in the area, 80 surface soil samples were collected for the analyses of heavy metal (such as mercury (Hg), arsenic (As), cadmium (Cd), lead (Pb), chromium (Cr) and copper (Cu)) contents and the environmental quality of the tea plantation soil. To classify and evaluate the sample analytical results, the Support Vector Machine (SVM) model coded in MATLAB was employed. Meanwhile, by comparing the result from Nemerow comprehensive pollution index method with that of fuzzy comprehensive evaluation method, the applicability of SVM in soil heavy metal pollution evaluation was discussed. These results show that,(1) There are significant spatial differences in soil heavy metal contents, with the variation coefficients in the order from high to low of Cr>Hg>Cu>As>Pb>Cd. By comparing the average value of evaluation factors with the soil background value of Guizhou Province, it is found that the values of Cd and Cu are lower than the soil background values, and the others fall in between the background values and the secondary standard values of the national soil quality standard. In fact, the chemical concentrations of 91.25% of the soil samples are below the standard limits for tea producing areas, which represents a soil environment of non-pollution and high-quality for tea plantation.(2) The quality of soil in the study area is good, with its soil environmental quality ranging between category I and II.The evaluation results of SVM method are quite similar to those of fuzzy comprehensive evaluation and Nemerow comprehensive pollution index methods, with a similarity of 82.5% and 80.0%, respectively. During the application of these methods it was found that the results of SVM were more accurate, which showed that the model is suitable for the evaluation of soil environmental quality in karst mountain area. (3)In addition, the SVM can solve complex nonlinear problems, with much easier manual operation and less artificial intervention, comparing with the application of traditional assessment models. It provides a new idea and method for the evaluation of soil environmental quality in karst mountain area.
- karst mountain area /
- Support Vector Machine /
- soil environmental quality

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