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

Abstract

Limited monitoring activities to assess data on heavy metal (HM) concentration contribute to worldwide concern for the environmental quality and the degree of toxicants in areas where there are elevated metals concentrations. Hence, this study used in-situ physicochemical parameters to the limited data on HM concentration in SW and GW. The site of the study was Marinduque Island Province in the Philippines, which experienced two mining disasters. Prediction model results showed that the SW models during the dry and wet seasons recorded a mean squared error (MSE) ranging from 6 × 10−7 to 0.070276. The GW models recorded a range from 5 × 10−8 to 0.045373, all of which were approaching the ideal MSE value of 0. Kling–Gupta efficiency values of developed models were all greater than 0.95. The developed neural network-particle swarm optimization (NN-PSO) models for SW and GW were compared to linear and support vector machine (SVM) models and previously published deterministic and artificial intelligence (AI) models. The findings indicated that the developed NN-PSO models are superior to the developed linear and SVM models, up to 1.60 and 1.40 times greater than the best model observed created by linear and SVM models for SW and GW, respectively. The developed models were also on par with previously published deterministic and AI-based models considering their prediction capability. Sensitivity analysis using Olden’s connection weights approach showed that pH influenced the concentration of HM significantly. Established on the research findings, it can be stated that the NN-PSO is an effective and practical approach in the prediction of HM concentration in water resources that contributes a solution to the limited HM concentration monitored data.

In Situ Measurements of Domestic Water Quality and Health Risks by Elevated Concentration of Heavy Metals and Metalloids Using Monte Carlo and MLGI Methods

“…The smallest mean CDI for adults was observed for Mn for all DW samples. The study of de Jesus et al (2021) in Marinduque also revealed high concentrations of Pb in the GW samples [58]. The HQ indices of metals through water intake in the study area are elaborated in Table 9.…”

“…The smallest mean CDI for adults was observed for Mn for all DW samples. The study of de Jesus et al (2021) in Marinduque also revealed high concentrations of Pb in the GW samples [ 58 ].…”

“…As the AIC value approached its minimal value, it was revealed that increasing the quantity of hidden neurons resulted in an increased AIC value. This indicated that the network had reached a state of generalization [ 58 ].…”

Design and Implementation of a Chain-Type Direct Push Drilling Rig for Contaminated Sites

“…As cities grow and more chemical factories move away, so do the number of contaminated sites and the types of contaminants. A contaminated site is one of the current study hotspots, such as the distribution characteristics [ 1 , 2 , 3 ], risk assessment [ 4 , 5 ], remediation [ 6 , 7 , 8 , 9 , 10 ], and impact on human health [ 11 , 12 ]. Due to the typical characteristics of hidden, irreversible, and complex contaminated sites in China [ 13 ], a contaminated sites investigation has received more attention.…”

Statistical Assessment, Modeling, and Mitigation of Water and Soil Pollution

“…Three articles focus on the examination of soil contamination [ 4 , 5 , 6 ] and the possible translocation of the pollutants from soil to plants [ 7 ], and the rest focus on surface and groundwater quality [ 8 , 9 , 10 ].…”