ArticleInternational journal of environmental research and public health2021
A Machine Learning Approach for Spatial Mapping of the Health Risk Associated with Arsenic-Contaminated Groundwater in Taiwan's Lanyang Plain.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 23 citations in OpenAlex.
- Applications of Machine Learning in the Research of Heavy Metal(loid)s-Related Risk: A Scoping Review of Methodology.Toxics · 2026Review
- Artificial Intelligence in Aquatic Biodiversity Research: A PRISMA-Based Systematic Review.Biology · 2025Review
- Geostatistical analysis and multivariate assessment of groundwater quality.Scientific reports · 2025Article
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Authors and funding
5 authors at 4 institutions in 2 countries.
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Abstract
Groundwater resources are abundant and widely used in Taiwan's Lanyang Plain. However, in some places the groundwater arsenic (As) concentrations far exceed the World Health Organization's standards for drinking water quality. Measurements of the As concentrations in groundwater show considerable spatial variability, which means that the associated risk to human health would also vary from region to region. This study aims to adapt a back-propagation neural network (BPNN) method to carry out more reliable spatial mapping of the As concentrations in the groundwater for comparison with the geostatistical ordinary kriging (OK) method results. Cross validation is performed to evaluate the prediction performance by dividing the As monitoring data into three sets. The cross-validation results show that the average determination coefficients (R
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