Research on identifying the outliers of the TDS in shallow groundwater based on the random forest model

doi:10.13745/j.esf.sf.2024.2.20

Abstract

Abstract:

Accurately identifying groundwater hydrochemical outliers caused by human activities is crucial for determining the nature background levels of groundwater chemical components and conducting rational assessment of groundwater pollution. The total dissolved solids (TDS), serving as a comprehensive indicator of groundwater hydrochemistry, its value directly reflect the quality of groundwater. Currently, the hydrochemical diagrams method has achieved favorable results in identifying outliers of TDS in groundwater. However, its fundamental principle is reverse identification based on the assumption that the hydrochemical type anomalies composed of the major ion components inevitably result in TDS anomalies, which will potentially leading to over-recognition during the anomaly identification. Therefore, the random forest model, commencing with a positive identification of the genesis mechanisms of TDS, combined with statistical method was employed to identify anomalies in TDS of shallow groundwater based in the Shaying River Basin. A comparative analysis of anomaly identification effectiveness among various methods was conducted, whose results demonstrated that the machine learning method could effectively identify the outliers of TDS in groundwater, with the identified TDS thresholds aligning with those derived from alternative methods. In contrast, the machine learning method, grounded in TDS genesis mechanisms, effectively identified anomalies by mitigating errors in hydrochemical diagrams. This approach successfully distinguished between high and low outliers, offering an alternative and efficacious method for TDS anomaly identification and expanding research perspectives on groundwater environmental background values.

Key words: groundwater environmental background values, TDS, outliers, machine learning method, the Shaying River Basin

CLC Number:

P641
X523

CHU Yanjia, HE Baonan, CHEN Zhen, HE Jiangtao. Research on identifying the outliers of the TDS in shallow groundwater based on the random forest model[J]. Earth Science Frontiers, 2025, 32(2): 456-468.

Figures/Tables 9

Fig.1 Workflow of machine learning method for identifying TDS outlier

Fig.2 Geographical location and sampling sites distribution of the Shaying River Basin

Fig.3 The spatial distribution and descriptive statistical of TDS and the 5 potential influencing factors

Fig.4 Results of the TDS outlier identification based on machine learning method

Table 1 Statistical results of the TDS outlier identification

TDS异常值识别方法	异常个数/ 个	最小值/ (mg·L^-1)	最大值/ (mg·L^-1)	平均数/ (mg·L^-1)	中位数/ (mg·L^-1)	5%分位数/ (mg·L^-1)	95%分位数/ (mg·L^-1)
剔除前		217.05	2 746.61	727.87	624.58	309.07	1 470.30
拉依达准则法剔除后	17	217.05	1 522.68	681.15	612.65	303.20	1 269.56
Piper三线图+拉依达准则法剔除后	125	217.05	1 522.68	671.35	609.62	285.51	1 263.27
机器学习法剔除后	61	217.05	1 642.08	653.94	598.03	309.07	1 169.69

Fig.5 Inflection point plot and box-plot of the TDS concentration distribution

Fig.6 The descriptive statistical and spatial distribution of the TDS outlier identification results of machine learning method

Fig.7 Analysis of the genesis of high and low outliers and the verification results of artificial pollution

Fig.8 Hydrochemical characteristics of high/low TDS outliers in groundwater

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