基于机器学习方法的地下水氨氮时空分布规律

Spatiotemporal Distribution of Groundwater Ammonia Nitrogen Based on Machine Learning Methods

氨氮是地下水中的主要无机污染物之一,其主要来自农业、工业和生活污染。过量的氨氮会危害人类健康。氨氮时空分布受气象、水文、水文地质和土地利用类型等因素的影响,因此,基于有限采样点的地下水氨氮分析会产生很大的不确定性。本研究以三江平原松花江流域为例,选取土壤有机质质量分数、土壤全氮质量分数、土壤阳离子交换容量(CEC)、土壤pH值、地下水埋深、包气带黏土层厚度和土地利用类型作为潜在的影响因素,建立拟合氨氮质量浓度的机器学习模型;在此基础上使用解释机器学习模型的SHAP(shapley additive explanations)方法识别显著的影响因素,并据此建立机器学习预测模型,对研究区地下水氨氮质量浓度进行数据插补,分析其时空变化规律。研究结果表明:地下水埋深、土地利用类型、CEC和土壤有机质质量分数是研究区地下水氨氮的主要影响因素;2011—2018年期间,研究区地下水氨氮处于Ⅰ—Ⅲ类水质级别的面积呈现增加趋势,面积占比从31%增加到87%,Ⅳ—Ⅴ类水的面积呈现减少趋势,面积占比从69%减少到13%,水质整体向好。

Ammonia nitrogen is one of the main inorganic pollutants in groundwater,which mainly comes from agricultural,industrialy and domestic pollution.Excessive ammonia nitrogen will endanger human health.Temporal and spatial distribution of ammonia nitrogen is affected by factors such as meteorology,hydrology,hydrogeology,and land use type,so groundwater ammonia nitrogen analysis based on limited sampling points will generate great uncertainty.In this study,firstly,the Songhua River basin in the Sanjiang Plain was taken as an example,and soil organic matter mass fraction,soil total nitrogen mass fraction,soil cation exchange capacity(CEC),soil pH value,groundwater depth,thickness of clay layer in vadose zone and land use type were selected as potential influencing factors,a machine learning model for fitting ammonia nitrogen concentration was established.Secondly,significant influencing factors were identified using the shapley additive explanations(SHAP)method of interpreting machine learning models.Finally,a machine learning prediction model was established according to the significant influencing factors,and the data of groundwater ammonia nitrogen in the study area was interpolated.And the temporal and spatial variation of ammonia nitrogen was analyzed.The results showed that groundwater depth,land use type,CEC and soil organic matter mass fraction were the main influencing factors of groundwater ammonia nitrogen in this area.The area of groundwater ammonia nitrogen in theⅠ-Ⅲwater quality level showed an increasing trend.The proportion of area increased from 31%to 87%.And the area ofⅣ-Ⅴwater quality showed a decreasing trend.The proportion of area decreased from 69%to 13%.The overall water quality was improved from 2011 to 2018.