中国沙漠 ›› 2018, Vol. 38 ›› Issue (3): 657-663.DOI: 10.7522/j.issn.1000-694X.2017.00026

• 水文与水资源 • 上一篇    下一篇


吴敏1,2, 温小虎1, 冯起1, 尹振良1, 杨林山1   

  1. 1. 中国科学院西北生态环境资源研究院, 甘肃 兰州 730000;
    2. 中国科学院大学, 北京 100049
  • 收稿日期:2017-01-23 修回日期:2017-04-11 出版日期:2018-05-20 发布日期:2018-11-06
  • 通讯作者: 温小虎(
  • 作者简介:吴敏(1993-),女,甘肃人,硕士研究生,从事地下水数值模拟研究。
  • 基金资助:

Assessment of Groundwater Quality Based on Random Forest Model in Arid Oasis Area

Wu Min1,2, Wen Xiaohu1, Feng Qi1, Yin Zhengliang1, Yang Linshan1   

  1. 1. Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China;
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2017-01-23 Revised:2017-04-11 Online:2018-05-20 Published:2018-11-06

摘要: 为合理准确评价地下水水质,建立了基于随机森林(RF)模型的地下水水质评价模型,并根据张掖盆地81个地下水采样点的pH值、Cl-、SO42-、NO3-、Na+、NH4+含量及总硬度的监测数据,对研究区的地下水水质进行了综合评价。结果表明:盆地地下水水质主要为Ⅱ、Ⅲ、Ⅳ类水,其中甘州区地下水埋藏较深,水体不容易受到来自地面的污染,水质较好,大多数地方为Ⅱ类水;临泽县和高台县地下水埋藏较浅,水质较差,大多数地方为Ⅲ类水,尤其高台县的水位最浅,再加上地处河段下游,污染更为严重,部分地区达到Ⅳ类。根据指标的重要性度量发现影响研究区域地下水水质的主要因子是NO3-含量;其次是NH4+、SO42-、Na+、Cl-含量及总硬度、pH值。为验证模型的有效性,将地下水水质评价结果与基于支持向量机(SVM)和人工神经网络(ANN)的地下水水质综合评价模型模拟结果进行对比,3个模型均能很好地评价研究区地下水水质,但RF模型的评价结果更为准确。

关键词: 水质评价, 地下水, 随机森林, 张掖盆地

Abstract: In this study, a groundwater quality evaluation model was established to assess the groundwater quality reasonably and accurately in the Zhangye Basin by using random forest model (RF). Based on the pH, Cl-, SO42-, NO3-, Na+, NH4+, and total hardness observation values of 81 groundwater sampling points in the basin, a comprehensive evaluation of groundwater quality for the whole study area was made. Results indicated that the water quality in the study area can be mainly classified into class Ⅱ, Ⅲ, and Ⅳ. Specifically, water quality in most of the local Ganzhou District was class Ⅱ because groundwater was difficult to be contaminated by surface for the deep water table. However, with shallow water level and poor water quality, most areas in Linze county and Gaotai county had class Ⅲ water quality, especially some areas in Gaotai county even reached class Ⅳ for the highest water level and being located in downstream of the river. Moreover, according to the index of importance, the main factor affecting the groundwater quality in the study area was found to be NO3- and the order of the other ions was NH4+, SO42-, Na+, Cl-, total hardness and pH successively. In order to test the validity of the developed model, comparisons were made to the support vector machine (SVM) model and the artificial neural network (ANN) model. Results showed that performances obtained by the three aforementioned models were satisfactory and RF model performed much better than the SVM and ANN models.

Key words: water quality evaluation, groundwater, random forest, Zhangye Basin