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首页> 外文期刊>Environmental Science & Technology >Machine Learning Models of Groundwater Arsenic Spatial Distribution in Bangladesh: Influence of Holocene Sediment Depositional History
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Machine Learning Models of Groundwater Arsenic Spatial Distribution in Bangladesh: Influence of Holocene Sediment Depositional History

机译：孟加拉国地下水砷空间分布机器学习模型：全新世泥沙沉积史的影响

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摘要

Recent advances in machine learning methods offer the opportunity to improve risk assessment and to decipher factors influencing the spatial variability of groundwater arsenic ([As]_(gw)). A systematic comparison reveals that boosted regression trees (BRT) and random forest (RF) outperform logistic regression. The probability of [As]_(gw) exceeding 5 μg/L (approximate median value of Bangladesh [As]_(gw)), 10 μg/L (WHO provisional guideline value), and SO μg/L (Bangladesh drinking water standard) is modeled by BRT and RF methods for Bangladesh and its four subregions demarcated by major rivers. Of the 109 geo- environmental and hydrochemical predictor variables, phosphorus and iron emerge as the most important across spatial scales, consistent with known As mobilization mechanisms. Well depth is significant only when hydrochemical parameters are not considered, consistent with prior studies. A peak of probability of [As]_(gw) exceedance at ～30 m depth is evident in the partial dependence plots (PDPs) for spatial-parameter-only models but not in the equivalent all-parameter models, suggesting that sediment depositional history explains interdependent spatial patterns of groundwater As-P-Fe in Holocene aquifers. The South region exhibits a decrease of probability of [As]_(gw) exceedance below 150 m depth in PDPs for spatial-parameter-only and all-parameter models, supporting that the deeper Pleistocene aquifer is a low-As water resource.
机译：机器学习方法的最新进展提供了改善风险评估的机会，并破译影响地下水砷的空间变异性的因素（[AS] _（GW））。系统的比较揭示了增强的回归树（BRT）和随机森林（RF）优于逻辑回归。 [AS] _（GW）超过5μg/ L的概率（孟加拉国的近似值[AS] _（GW）），10μg/ L（临时指南值），如μg/ L（孟加拉国饮用水标准）由BRT和RF方法为孟加拉国和主要河流划分的四个次区域。在109个地理环境和水化学性预测因子变量中，磷和铁作为空间鳞片中最重要的，这与称为动员机制一致。只有当不考虑水化学参数时，井深度才会与先前的研究一致。 [AS] _（GW）的概率达到〜30米深度的峰值是在空间参数的部分依赖性地块（PDP）中明显明显，但不在等效的All-参数模型中，表明沉积物沉积历史解释全茂性含水层地下水AS-FE的相互依存空间模式。南部区域的概率下降概率在PDPS低于150米的PDPS中，用于空间参数和All-参数模型，支持更深的更新者含水层是低于水资源的。

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  • 来源
    《Environmental Science & Technology》 |2020年第15期|9454-9463|共10页
  • 作者

    Zhen Tan; Qiang Yang; Yan Zheng;

  • 作者单位

    College of Engineering Peking University Beijing 100871 China Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control School of Environmental Science and Engineering and State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen 518055 China;

    Lamont-Doherty Earth Observatory of Columbia University Palisades New York 10964 United States;

    Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control School of Environmental Science and Engineering and State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen 518055 China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
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