Development of enhanced groundwater arsenic prediction model using machine learning approaches in Southeast Asian countries
Cited 0 times inCited 0 times in
- Development of enhanced groundwater arsenic prediction model using machine learning approaches in Southeast Asian countries
- Park, Yongeun; Ligaray, Mayzonee; Kim, Young Mo; Kim, Joon Ha; Cho, Kyung Hwa; Sthiannopkao, Suthipong
- Issue Date
- DESALINATION PUBL
- DESALINATION AND WATER TREATMENT, v.57, no.26, pp.12227 - 12236
- Groundwater contamination with arsenic (As) is one of the major issues in the world, especially for Southeast Asian (SEA) countries where groundwater is the major drinking water source, especially in rural areas. Unfortunately, quantification of groundwater As contamination is another burden for those countries because it requires sophisticated equipment, expensive analysis, and well-trained technicians. Here, we collected approximately 350 groundwater samples from three different SEA countries, including Cambodia, Lao PDR, and Thailand, in an attempt to quantify total As concentrations and conventional water quality variables. After that, two machine learning models (i.e. artificial neural network (ANN) and support vector machine (SVM)) were applied to predict groundwater As contamination using conventional water quality parameters. Prior to modeling approaches, the pattern search algorithm in MATLAB software was used to optimize the ANN and SVM model parameters, attempting to find the best parameters set for modeling groundwater As concentrations. Overall, the SVM showed the superior prediction performance, giving higher Nash-Sutcliffe coefficients than ANN in both the training and validation periods. We hope that the model developed by this study could be a suitable quantification tool for groundwater As contamination in SEA countries. © 2015 Balaban Desalination Publications. All rights reserved
- Appears in Collections:
- UEE_Journal Papers
- Files in This Item:
- There are no files associated with this item.
can give you direct access to the published full text of this article. (UNISTARs only)
Show full item record
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.