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

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

Abstract Arsenic (As) is a well‐known carcinogen and chemical contaminant in groundwater. The spatial heterogeneity in As distribution in groundwater makes it difficult to predict the location of safe areas for tube well installations, consumption, and agriculture. Geospatial machine learning techni...

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Bibliographic Details
Main Authors: Bibhash Nath, Runti Chowdhury, Wenge Ni‐Meister, Chandan Mahanta
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2022-03-01
Series:GeoHealth
Subjects:
arsenic
predictive modeling
random forest model
machine learning
Assam
Online Access:https://doi.org/10.1029/2021GH000585

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