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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Toxics |
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| Online Access: | https://www.mdpi.com/2305-6304/9/11/273 |
| _version_ | 1797508407264018432 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:01:39Z |
| format | Article |
| id | doaj.art-ed1b24d56d10401da8c70222ca0007f5 |
| institution | Directory Open Access Journal |
| issn | 2305-6304 |
| language | English |
| last_indexed | 2024-03-10T05:01:39Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Toxics |
| spelling | doaj.art-ed1b24d56d10401da8c70222ca0007f52023-11-23T01:47:31ZengMDPI AGToxics2305-63042021-10-0191127310.3390/toxics9110273A Hybrid Neural Network–Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the PhilippinesKevin Lawrence M. De Jesus0Delia B. Senoro1Jennifer C. Dela Cruz2Eduardo B. Chan3School of Graduate Studies, Mapua University, Manila 1002, PhilippinesSchool of Graduate Studies, Mapua University, Manila 1002, PhilippinesSchool of Graduate Studies, Mapua University, Manila 1002, PhilippinesDyson College of Arts and Science, Pace University, New York, NY 10038, USAWater 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.https://www.mdpi.com/2305-6304/9/11/273groundwateracid mine drainageheavy metalsphysicochemical characteristicsneural networkparticle swarm optimization |
| spellingShingle | 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 Toxics groundwater acid mine drainage heavy metals physicochemical characteristics neural network particle swarm optimization |
| 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_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_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_sort | hybrid neural network particle swarm optimization informed spatial interpolation technique for groundwater quality mapping in a small island province of the philippines |
| topic | groundwater acid mine drainage heavy metals physicochemical characteristics neural network particle swarm optimization |
| url | https://www.mdpi.com/2305-6304/9/11/273 |
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