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A Machine Learning Approach for Spatial Mapping of the Health Risk Associated with Arsenic-Contaminated Groundwater in Taiwan’s Lanyang Plain

Author

Listed:
  • Ching-Ping Liang

    (Department of Nursing, Fooyin University, Kaohsiung City 831, Taiwan)

  • Chi-Chien Sun

    (Graduate Institute of Applied Geology, National Central University, Taoyuan City 320, Taiwan)

  • Heejun Suk

    (Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, Korea)

  • Sheng-Wei Wang

    (Department of Water Resources and Environmental Engineering, Tamkang University, New Taipei City 251, Taiwan)

  • Jui-Sheng Chen

    (Graduate Institute of Applied Geology, National Central University, Taoyuan City 320, Taiwan)

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 2 ) for the As concentrations obtained with BPNN and OK are 0.55 and 0.49, whereas the average root mean square errors (RMSE) are 0.49 and 0.54, respectively. Given the better prediction performance of the BPNN, it is recommended as a more reliable tool for the spatial mapping of the groundwater As concentration. Subsequently, the As concentrations estimated obtained using the BPNN are applied to develop a spatial map illustrating the risk to human health associated with the ingestion of As-containing groundwater based on the noncarcinogenic hazard quotient (HQ) and carcinogenic target risk (TR) standards established by the U.S. Environmental Protection Agency. Such maps can be used to demarcate the areas where residents are at higher risk due to the ingestion of As-containing groundwater, and prioritize the areas where more intensive monitoring of groundwater quality is required. The spatial mapping of As concentrations from the BPNN was also used to demarcate the regions where the groundwater is suitable for farmland and fishponds based on the water quality standards for As for irrigation and aquaculture.

Suggested Citation

  • Ching-Ping Liang & Chi-Chien Sun & Heejun Suk & Sheng-Wei Wang & Jui-Sheng Chen, 2021. "A Machine Learning Approach for Spatial Mapping of the Health Risk Associated with Arsenic-Contaminated Groundwater in Taiwan’s Lanyang Plain," IJERPH, MDPI, vol. 18(21), pages 1-15, October.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:21:p:11385-:d:667939
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    References listed on IDEAS

    as
    1. Zhenyi Jia & Shenglu Zhou & Quanlong Su & Haomin Yi & Junxiao Wang, 2017. "Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loid)s Pollution Based on Kriging Interpolation and BP Neural Network," IJERPH, MDPI, vol. 15(1), pages 1-14, December.
    2. Ching-Ping Liang & Wen-Shuo Hsu & Yi-Chi Chien & Sheng-Wei Wang & Jui-Sheng Chen, 2019. "The Combined Use of Groundwater Quality, Drawdown Index and Land Use to Establish a Multi-Purpose Groundwater Utilization Plan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(12), pages 4231-4247, September.
    3. Ching-Ping Liang & Yi-Chi Chien & Cheng-Shin Jang & Ching-Fang Chen & Jui-Sheng Chen, 2017. "Spatial Analysis of Human Health Risk Due to Arsenic Exposure through Drinking Groundwater in Taiwan’s Pingtung Plain," IJERPH, MDPI, vol. 14(1), pages 1-16, January.
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