| 基于随机森林模型的地下水水质评价方法 |
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| 引用本文： | 闫佰忠,孙剑,安娜.基于随机森林模型的地下水水质评价方法[J].水电能源科学,2019,37(11):66-69. |
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| 作者姓名： | 闫佰忠 孙剑 安娜 |
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| 作者单位： | 1. 河北地质大学 a. 水资源与环境学院； b. 河北省水资源可持续利用与开发重点实验室， 河北 石家庄 050031；2. 河北省水资源可持续利用与产业结构化协同创新中心， 河北 石家庄 050031； 3. 河北地矿局 国土资源勘查中心， 河北 石家庄 050031 |
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| 基金项目： | 中国博士后基金面上项目（2018M631874）；河北省教育厅重点基金项目（ZD2019082）；河北省地质资源环境监测与保护重点实验室开放课题项目（JCYKT201901）；河北省自然科学基金（D2018403040）；河北地质大学博士科研启动基金（BQ2017011）；河北省教育厅自然青年基金（QN2017026）；河北省水利科技计划（2017-59） |
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| 摘 要： | 为获得更加高效和稳定的地下水水质评价方法,基于安阳市8个地下水监测点的水质检测数据,利用基于随机森林模型的地下水水质评价模型对该地区地下水水质进行综合评价,并与BP神经网络模型结果进行比较。结果表明,8个地下水监测点的水质主要为Ⅱ、Ⅲ类水,其中位于安阳市汤阴县、内黄县、滑县的监测点水质较差,生活污染及工业污染是该区域导致水质变差的关键。通过对比两个模型评价流程及结果,发现基于随机森林模型的水质评价模型能够准确评价水质的同时,拥有更高训练效率与稳定性。
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| 关 键 词： | 随机森林模型 神经网络 地下水水质评价 安阳市 |
Assessment of Groundwater Quality Based on Random Forest Model |
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| Abstract: | The groundwater quality in Anyang City was evaluated comprehensively by using the groundwater quality evaluation model based on stochastic forest model, and compared with the BP neural network model in this paper. The results show that the groundwater quality of the eight groundwater monitoring sites were mainly class II and III. The monitoring sites located in Tangyin, Neihuang and Huaxian of Anyang country have poor groundwater quality. The key factors leading to the deterioration of water quality in this area are domestic pollution and industrial pollution. By comparing the evaluation process and results of the two models, it is found that the groundwater quality evaluation model based on stochastic forest model can accurately evaluate water quality, and has higher training efficiency and stability. This study has important theoretical and practical significance for development of groundwater quality evaluation theory and methods. |
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| Keywords: | stochastic forest model neural network groundwater quality assessment Anyang City |
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