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

Abstract

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.

Health Risks Due to Metal Concentrations in Soil and Vegetables from the Six Municipalities of the Island Province in the Philippines

“…In addition, though Cd was not detected in soil, the Cd concentration in vegetables shows that it is spatially correlated to nearby environmental media having a Moran's I and p-value of 0.668301 and 0.004269, respectively. This is attributed to the groundwater [21,65] and runoff quality [65]. As shown in Table 9, the correlation coefficients of Cr-Cd, Mn-Fe, Ni-Fe, Ni-Mn, Pb-Cd, and Pb-Cr were 0.986, 0.629, 0.605, 0.852, 0.977, and 0.977 (p < 0.01), respectively.…”

“…These elevated metal concentrations in the vegetables that posed HHR to the population were associated to the existence of two abandoned open mine pits which are located at the higher elevation of Marinduque [69]. Groundwater flow [21] through porous media, and runoff overflow in the island province potentially caused the elevated concentration of metals in soil that the vegetables may have absorbed from the soil. Given the above information, it is advised to exercise a 75% temporary consumption reduction of bitter melon, eggplant, string beans, and sweet potato tops produced in the municipalities of Buenavista and Gasan.…”

“…In addition, though Cd was not detected in soil, the Cd concentration in vegetables shows that it is spatially correlated to nearby environmental media having a Moran's I and p-value of 0.668301 and 0.004269, respectively. This is attributed to the groundwater [21,65] and runoff quality [65].…”

“…There have been several studies on heavy metal contamination in water and sediment [21][22][23][24] in the island province; however, metal concentrations in soil and vegetables were not examined. Other studies focused on phytoremediation strategies and techniques [25][26][27][28][29].…”

Neuro-Particle Swarm Optimization Based In-Situ Prediction Model for Heavy Metals Concentration in Groundwater and Surface Water

“…The collected surface water and groundwater samples during the dry season (DS) and wet season (WS) were mapped using an MLGI method [67]. This included physicochemical parameters and heavy metal concentrations of SW and GW.…”