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

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

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 physicochemica...

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Bibliographic Details
Main Authors: Delia B. Senoro, Kevin Lawrence M. de Jesus, Leonel C. Mendoza, Enya Marie D. Apostol, Katherine S. Escalona, Eduardo B. Chan
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Applied Sciences
Subjects:
groundwater
heavy metals
physicochemical parameters
in-situ
machine learning
geostatistical analysis
Online Access:https://www.mdpi.com/2076-3417/12/1/132

Internet

https://www.mdpi.com/2076-3417/12/1/132

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