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

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 sp...

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Autores principales:	Ching-Ping Liang, Chi-Chien Sun, Heejun Suk, Sheng-Wei Wang, Jui-Sheng Chen
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	back-propagation neural network ordinary kriging groundwater arsenic contamination hazard quotient target risk Medicine R
Acceso en línea:	https://doaj.org/article/98601a5e8f014185b6cd68037ec0bbe0
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id	oai:doaj.org-article:98601a5e8f014185b6cd68037ec0bbe0
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spelling	oai:doaj.org-article:98601a5e8f014185b6cd68037ec0bbe02021-11-11T16:30:18ZA Machine Learning Approach for Spatial Mapping of the Health Risk Associated with Arsenic-Contaminated Groundwater in Taiwan’s Lanyang Plain10.3390/ijerph1821113851660-46011661-7827https://doaj.org/article/98601a5e8f014185b6cd68037ec0bbe02021-10-01T00:00:00Zhttps://www.mdpi.com/1660-4601/18/21/11385https://doaj.org/toc/1661-7827https://doaj.org/toc/1660-4601Groundwater 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<sup>2</sup>) 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.Ching-Ping LiangChi-Chien SunHeejun SukSheng-Wei WangJui-Sheng ChenMDPI AGarticleback-propagation neural networkordinary kriginggroundwater arsenic contaminationhazard quotienttarget riskMedicineRENInternational Journal of Environmental Research and Public Health, Vol 18, Iss 11385, p 11385 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	back-propagation neural network ordinary kriging groundwater arsenic contamination hazard quotient target risk Medicine R
spellingShingle	back-propagation neural network ordinary kriging groundwater arsenic contamination hazard quotient target risk Medicine R Ching-Ping Liang Chi-Chien Sun Heejun Suk Sheng-Wei Wang Jui-Sheng Chen A Machine Learning Approach for Spatial Mapping of the Health Risk Associated with Arsenic-Contaminated Groundwater in Taiwan’s Lanyang Plain
description	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<sup>2</sup>) 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.
format	article
author	Ching-Ping Liang Chi-Chien Sun Heejun Suk Sheng-Wei Wang Jui-Sheng Chen
author_facet	Ching-Ping Liang Chi-Chien Sun Heejun Suk Sheng-Wei Wang Jui-Sheng Chen
author_sort	Ching-Ping Liang
title	A Machine Learning Approach for Spatial Mapping of the Health Risk Associated with Arsenic-Contaminated Groundwater in Taiwan’s Lanyang Plain
title_short	A Machine Learning Approach for Spatial Mapping of the Health Risk Associated with Arsenic-Contaminated Groundwater in Taiwan’s Lanyang Plain
title_full	A Machine Learning Approach for Spatial Mapping of the Health Risk Associated with Arsenic-Contaminated Groundwater in Taiwan’s Lanyang Plain
title_fullStr	A Machine Learning Approach for Spatial Mapping of the Health Risk Associated with Arsenic-Contaminated Groundwater in Taiwan’s Lanyang Plain
title_full_unstemmed	A Machine Learning Approach for Spatial Mapping of the Health Risk Associated with Arsenic-Contaminated Groundwater in Taiwan’s Lanyang Plain
title_sort	machine learning approach for spatial mapping of the health risk associated with arsenic-contaminated groundwater in taiwan’s lanyang plain
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/98601a5e8f014185b6cd68037ec0bbe0
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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

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