Predicting the Distribution of Arsenic in Groundwater by a Geospatial Machine Learning Technique in the Two Most Affected Districts of Assam, India: The Public Health Implications-Reference-Cited by-同舟云学术

Predicting the Distribution of Arsenic in Groundwater by a Geospatial Machine Learning Technique in the Two Most Affected Districts of Assam, India: The Public Health Implications

Published:2022-03 Issue:3 Volume:6 Page:
ISSN:2471-1403
Container-title:GeoHealth
language:en
Short-container-title:GeoHealth

Author:

Nath Bibhash¹^ORCID,Chowdhury Runti²,Ni‐Meister Wenge¹,Mahanta Chandan³

Affiliation:

1. Department of Geography and Environmental Science Hunter College of City University of New York New York NY USA

2. Department of Geological Sciences Gauhati University Guwahati India

3. Department of Civil Engineering Indian Institute of Technology Guwahati Guwahati India

Funder

National Aeronautics and Space Administration

Publisher

American Geophysical Union (AGU)

Subject

Health, Toxicology and Mutagenesis,Management, Monitoring, Policy and Law,Public Health, Environmental and Occupational Health,Pollution,Waste Management and Disposal,Water Science and Technology,Epidemiology,Global and Planetary Change

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021GH000585

Reference57 articles.

1. Arsenic enrichment in groundwater of the alluvial aquifers in Bangladesh: an overview

2. Statistical Modeling of Global Geogenic Arsenic Contamination in Groundwater

3. Modeling the Probability of Arsenic in Groundwater in New England as a Tool for Exposure Assessment

4. Predicting groundwater arsenic contamination: Regions at risk in highest populated state of India

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