دورية أكاديمية
أعرض في EDS

Mapped Predictions of Manganese and Arsenic in an Alluvial Aquifer Using Boosted Regression Trees.

التفاصيل البيبلوغرافية
العنوان: Mapped Predictions of Manganese and Arsenic in an Alluvial Aquifer Using Boosted Regression Trees.
المؤلفون: Knierim KJ, Kingsbury JA; U.S. Geological Survey, Nashville, TN, USA., Belitz K; U.S. Geological Survey, Carlisle, MA, USA., Stackelberg PE; U.S. Geological Survey, Troy, NY, USA., Minsley BJ; U.S. Geological Survey, Denver, CO, USA., Rigby JR; U.S. Geological Survey, Oxford, MS, USA.
المصدر: Ground water [Ground Water] 2022 May; Vol. 60 (3), pp. 362-376. Date of Electronic Publication: 2022 Jan 07.
نوع المنشور: Journal Article; Research Support, U.S. Gov't, Non-P.H.S.
اللغة: English
بيانات الدورية: Publisher: Blackwell Publishing Country of Publication: United States NLM ID: 9882886 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1745-6584 (Electronic) Linking ISSN: 0017467X NLM ISO Abbreviation: Ground Water Subsets: MEDLINE
أسماء مطبوعة: Publication: 2005- : Malden, MA : Blackwell Publishing
Original Publication: Worthington, Ohio : Water Well Journal Pub. Co.,
مواضيع طبية MeSH: Arsenic*/analysis , Groundwater* , Water Pollutants, Chemical*/analysis, Environmental Monitoring ; Manganese/analysis
مستخلص: Manganese (Mn) concentrations and the probability of arsenic (As) exceeding the drinking-water standard of 10 μg/L were predicted in the Mississippi River Valley alluvial aquifer (MRVA) using boosted regression trees (BRT). BRT, a type of ensemble-tree machine-learning model, were created using predictor variables that affect Mn and As distribution in groundwater. These variables included iron (Fe) concentrations and specific conductance predicted from previously developed BRT models, groundwater flux and age estimates from MODFLOW, and hydrologic characteristics. The models also included results from the first airborne geophysical survey conducted in the United States to target an entire aquifer system. Predictions of high Mn and As occurred where Fe was high. Predicted high Mn concentrations were correlated with fraction of young groundwater (less than 65 years) computed from MODFLOW results. High probabilities of As exceedance were predicted where groundwater was relatively old and airborne electromagnetic resistivity was high, typically proximal to streams. Two-variable partial-dependence plots and sensitivity analysis were used to provide insight into the factors controlling Mn and As distribution in groundwater. The maps of predicted Mn concentrations and As exceedance probabilities can be used to identify areas where these constituents may be high, and that could be targeted for further study. This paper shows that incorporation of a selected set of process-informed data, such as MODFLOW results and airborne geophysics, into a machine-learning model improves model interpretability. Incorporation of process-rich information into machine-learning models will likely be useful for addressing a wide range of problems of interest to groundwater hydrologists.
(Published 2022. This article is a U.S. Government work and is in the public domain in the USA. Groundwater published by Wiley Periodicals LLC on behalf of National Ground Water Association.)
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المشرفين على المادة: 0 (Water Pollutants, Chemical)
42Z2K6ZL8P (Manganese)
N712M78A8G (Arsenic)
تواريخ الأحداث: Date Created: 20211224 Date Completed: 20220506 Latest Revision: 20220731
رمز التحديث: 20240628
مُعرف محوري في PubMed: PMC9302655
DOI: 10.1111/gwat.13164
PMID: 34951475
قاعدة البيانات: MEDLINE
الوصف
تدمد:1745-6584
DOI:10.1111/gwat.13164