Groundwater Quality Monitoring Using In-Situ Measurements and Hybrid Machine Learning with Empirical Bayesian Kriging Interpolation Method

Applied Sciences

Eduardo B. Chan¹  Enya Marie D. Apostol²  Leonel C. Mendoza²  Katherine S. Escalona²  Delia B. Senoro³  Kevin Lawrence M. de Jesus⁴ 

[1] Dyson College of Arts and Sciences, Pace University, New York, NY 10038, USA;Resiliency and Sustainable Development Center, Yuchengco Innovation Center, Mapua University, Manila 1002, Philippines;School of Civil, Environmental and Geological Engineering, Mapua University, Intramuros, Manila 1002, Philippines;School of Graduate Studies, Mapua University, Intramuros, Manila 1002, Philippines;

关键词: groundwater;    heavy metals;    physicochemical parameters;    in-situ;    machine learning;    geostatistical analysis;   

DOI  :  10.3390/app12010132

来源: DOAJ

【 摘 要 】

This article discusses the assessment of groundwater quality using a hybrid technique that would aid in the convenience of groundwater (GW) quality monitoring. Twenty eight (28) GW samples representing 62 barangays in Calapan City, Oriental Mindoro, Philippines were analyzed for their physicochemical characteristics and heavy metal (HM) concentrations. The 28 GW samples were collected at suburban sites identified by the coordinates produced by Global Positioning System Montana 680. The analysis of heavy metal concentrations was conducted onsite using portable handheld X-Ray Fluorescence (pXRF) Spectrometry. Hybrid machine learning—geostatistical interpolation (MLGI) method, specific to neural network particle swarm optimization with Empirical Bayesian Kriging (NN-PSO+EBK), was employed for data integration, GW quality spatial assessment and monitoring. Spatial map of metals concentration was produced using the NN-PSO-EBK. Another, spot map was created for observed metals concentration and was compared to the spatial maps. Results showed that the created maps recorded significant results based on its MSEs with values such as 1.404 × 10⁻⁴, 5.42 × 10⁻⁵, 6.26 × 10⁻⁴, 3.7 × 10⁻⁶, 4.141 × 10⁻⁴ for Ba, Cu, Fe, Mn, Zn, respectively. Also, cross-validation of the observed and predicted values resulted to R values range within 0.934–0.994 which means almost accurate. Based on these results, it can be stated that the technique is efficient for groundwater quality monitoring. Utilization of this technique could be useful in regular and efficient GW quality monitoring.

【 授权许可】

Unknown