Groundwater Quality Monitoring Using In-Situ Measurements and Hybrid Machine Learning with Empirical Bayesian Kriging Interpolation Method
| Autor: | Delia B. Senoro, Kevin Lawrence M. de Jesus, Leonel C. Mendoza, Enya Marie D. Apostol, Katherine S. Escalona, Eduardo B. Chan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Fluid Flow and Transfer Processes
Technology QH301-705.5 Process Chemistry and Technology geostatistical analysis Physics QC1-999 physicochemical parameters General Engineering groundwater heavy metals in-situ machine learning Engineering (General). Civil engineering (General) Computer Science Applications Chemistry General Materials Science TA1-2040 Biology (General) Instrumentation QD1-999 |
| Zdroj: | Applied Sciences, Vol 12, Iss 132, p 132 (2022) Applied Sciences; Volume 12; Issue 1; Pages: 132 |
| ISSN: | 2076-3417 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|