Machine learning predictions of nitrate in groundwater used for drinking supply in the conterminous United States
TLDR
In this article , a three-dimensional extreme gradient boosting (XGB) machine learning model was developed to predict the distribution of nitrate in groundwater in the conterminous United States (CONUS).About:
This article is published in Science of The Total Environment.The article was published on 2022-02-01 and is currently open access. It has received 23 citations till now. The article focuses on the topics: Nitrate & Groundwater.read more
Citations
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Effect of hydrogeochemical behavior on groundwater resources in Holocene aquifers of moribund Ganges Delta, India: Infusing data-driven algorithms.
TL;DR: In this paper , a field-based hydrogeochemical analysis has been carried out in the elevated arsenic prone areas of moribund Ganges delta, West Bengal, a part of western Ganga- Brahmaputra delta (GBD).
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Explainable step-wise binary classification for the susceptibility assessment of geo-hydrological hazards
Ömer Ekmekcioğlu,Kerim Koc +1 more
TL;DR: In this article , the authors proposed a novel step-wise binary prediction framework for the susceptibility assessment of geo-hydrological hazards specific to floods and landslides in the Kentucky River basin, United States.
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Exploring the additional value of class imbalance distributions on interpretable flash flood susceptibility prediction in the Black Warrior River basin, Alabama, United States
TL;DR: In this paper , the authors proposed a novel flash flood susceptibility prediction framework with a particular emphasis on the extent of imbalance between the number of flooding and non-flooding events.
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Machine learning predictions of chlorophyll-a in the Han river basin, Korea.
Kyung-Min Kim,Johng-Hwa Ahn +1 more
TL;DR: In this article , the authors developed a model to predict concentrations of chlorophyll-a ([Chl-a]) as a proxy for algal population with data from multiple monitoring stations in the Han river basin, by using machine-learning predictive models.
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Predicting Regional-Scale Elevated Groundwater Nitrate Contamination Risk Using Machine Learning on Natural and Human-Induced Factors
Soumyajit Sarkar,Abhijit Mukherjee,Srimanti Dutta Gupta,Soumendra N. Bhanja,Animesh Bhattacharya +4 more
TL;DR: In this paper , using machine learning models (Random Forest, Boosted Regression Tree, and Logistic Regression) on a large, in situ dataset, the authors have predicted the first nationwide extent of groundwater nitrate contamination risk (concentration >45 mg/L) across India.
References
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Stochastic gradient boosting
TL;DR: It is shown that both the approximation accuracy and execution speed of gradient boosting can be substantially improved by incorporating randomization into the procedure.
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A working guide to boosted regression trees
TL;DR: This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fit a single parsimonious model.
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Epidemiologic Evaluation of Measurement Data in the Presence of Detection Limits
Jay H. Lubin,Joanne S. Colt,David Camann,Scott Davis,James R. Cerhan,Richard K. Severson,Leslie Bernstein,Patricia Hartge +7 more
TL;DR: In this article, the authors consider the regression of an environmental measurement (dependent variable) on several covariates (independent variables) and find that the fill-in approach generally produces unbiased parameter estimates but may produce biased variance estimates and thereby distort inference when 30% or more of the data are below detection limits.
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Drinking Water Nitrate and Human Health: An Updated Review
Mary H. Ward,Rena R. Jones,Jean D. Brender,Theo M. de Kok,Peter J. Weyer,Bernard T. Nolan,Cristina M. Villanueva,Simone G. J. van Breda +7 more
TL;DR: The strongest evidence for a relationship between drinking water nitrate ingestion and adverse health outcomes (besides methemoglobinemia) is for colorectal cancer, thyroid disease, and neural tube defects.
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Nitrate in Groundwater of the United States, 1991−2003
TL;DR: Overall, nitrate concentrations in groundwater are most significantly affected by redox conditions, followed by nonpoint-source N inputs, followedBy other water-quality indicators and physical variables had a secondary influence on nitrates concentrations.