A Hybrid Neural Network–Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines

Water quality monitoring demands the use of spatial interpolation techniques due to on-ground challenges. The implementation of various spatial interpolation methods results in significant variations from the true spatial distribution of water quality in a specific location. The aim of this research...

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Autores principales:	Kevin Lawrence M. De Jesus, Delia B. Senoro, Jennifer C. Dela Cruz, Eduardo B. Chan
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	groundwater acid mine drainage heavy metals physicochemical characteristics neural network particle swarm optimization Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/ed1b24d56d10401da8c70222ca0007f5
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spelling	oai:doaj.org-article:ed1b24d56d10401da8c70222ca0007f52021-11-25T19:07:58ZA Hybrid Neural Network–Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines10.3390/toxics91102732305-6304https://doaj.org/article/ed1b24d56d10401da8c70222ca0007f52021-10-01T00:00:00Zhttps://www.mdpi.com/2305-6304/9/11/273https://doaj.org/toc/2305-6304Water quality monitoring demands the use of spatial interpolation techniques due to on-ground challenges. The implementation of various spatial interpolation methods results in significant variations from the true spatial distribution of water quality in a specific location. The aim of this research is to improve mapping prediction capabilities of spatial interpolation algorithms by using a neural network with the particle swarm optimization (NN-PSO) technique. Hybrid interpolation approaches were evaluated and compared by cross-validation using mean absolute error (MAE) and Pearson’s correlation coefficient (R). The governing interpolation techniques for the physicochemical parameters of groundwater (GW) and heavy metal concentrations were the geostatistical approaches combined with NN-PSO. The best methods for physicochemical characteristics and heavy metal concentrations were observed to have the least MAE and R values, ranging from 1.7 to 4.3 times and 1.2 to 5.6 times higher than the interpolation technique without the NN-PSO for the dry and wet season, respectively. The hybrid interpolation methods exhibit an improved performance as compared to the non-hybrid methods. The application of NN-PSO technique to spatial interpolation methods was found to be a promising approach for improving the accuracy of spatial maps for GW quality.Kevin Lawrence M. De JesusDelia B. SenoroJennifer C. Dela CruzEduardo B. ChanMDPI AGarticlegroundwateracid mine drainageheavy metalsphysicochemical characteristicsneural networkparticle swarm optimizationChemical technologyTP1-1185ENToxics, Vol 9, Iss 273, p 273 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	groundwater acid mine drainage heavy metals physicochemical characteristics neural network particle swarm optimization Chemical technology TP1-1185
spellingShingle	groundwater acid mine drainage heavy metals physicochemical characteristics neural network particle swarm optimization Chemical technology TP1-1185 Kevin Lawrence M. De Jesus Delia B. Senoro Jennifer C. Dela Cruz Eduardo B. Chan A Hybrid Neural Network–Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines
description	Water quality monitoring demands the use of spatial interpolation techniques due to on-ground challenges. The implementation of various spatial interpolation methods results in significant variations from the true spatial distribution of water quality in a specific location. The aim of this research is to improve mapping prediction capabilities of spatial interpolation algorithms by using a neural network with the particle swarm optimization (NN-PSO) technique. Hybrid interpolation approaches were evaluated and compared by cross-validation using mean absolute error (MAE) and Pearson’s correlation coefficient (R). The governing interpolation techniques for the physicochemical parameters of groundwater (GW) and heavy metal concentrations were the geostatistical approaches combined with NN-PSO. The best methods for physicochemical characteristics and heavy metal concentrations were observed to have the least MAE and R values, ranging from 1.7 to 4.3 times and 1.2 to 5.6 times higher than the interpolation technique without the NN-PSO for the dry and wet season, respectively. The hybrid interpolation methods exhibit an improved performance as compared to the non-hybrid methods. The application of NN-PSO technique to spatial interpolation methods was found to be a promising approach for improving the accuracy of spatial maps for GW quality.
format	article
author	Kevin Lawrence M. De Jesus Delia B. Senoro Jennifer C. Dela Cruz Eduardo B. Chan
author_facet	Kevin Lawrence M. De Jesus Delia B. Senoro Jennifer C. Dela Cruz Eduardo B. Chan
author_sort	Kevin Lawrence M. De Jesus
title	A Hybrid Neural Network–Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines
title_short	A Hybrid Neural Network–Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines
title_full	A Hybrid Neural Network–Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines
title_fullStr	A Hybrid Neural Network–Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines
title_full_unstemmed	A Hybrid Neural Network–Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines
title_sort	hybrid neural network–particle swarm optimization informed spatial interpolation technique for groundwater quality mapping in a small island province of the philippines
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ed1b24d56d10401da8c70222ca0007f5
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A Hybrid Neural Network–Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines

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