Prediction of Groundwater Quality Using Seven Types of First-Order Univariate Grey Model in the Chishan Basin, Taiwan

This study represents the first report of the innovative use of seven types of first-order univariate grey model, abbreviated as GM (1, 1) model, for comprehensive groundwater quality prediction in the Chishan basin of Taiwan in which some districts exhibit a certain level of risk for flood and drou...

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Bibliographic Details
Published in:Water, air, and soil pollution air, and soil pollution, 2022-12, Vol.233 (12), p.481, Article 481
Main Authors: Pai, Tzu-Yi, Wu, Ray-Shyan, Chen, Ching-Ho, Lo, Huang-Mu, Wan, Terng-Jou, Liu, Min-Hsin, Chen, Wei-Cheng, Lin, Yi-Ping, Hsu, Chun-Tse
Format: Article
Language:English
Subjects:
Alkalinity
Ammonia
Analysis
Atmospheric Protection/Air Quality Control/Air Pollution
Climate change
Climate Change/Climate Change Impacts
Dissolved solids
Drought
Droughts
Earth and Environmental Science
Environment
Environmental monitoring
Global temperature changes
Groundwater
Groundwater quality
Hydrogeology
Manganese
Organic carbon
Predictions
Risk levels
Risk reduction
Soil Science & Conservation
Total dissolved solids
Total organic carbon
Water hardness
Water quality
Water Quality/Water Pollution
Water shortages
Water, Underground
Water-supply
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Summary:This study represents the first report of the innovative use of seven types of first-order univariate grey model, abbreviated as GM (1, 1) model, for comprehensive groundwater quality prediction in the Chishan basin of Taiwan in which some districts exhibit a certain level of risk for flood and drought. The results indicated that GM (1, 1) model was applicable to the prediction of groundwater quality. The prediction performance showed that the mean absolute percentage errors (MAPEs) for pH and temperature were less than 10%, indicating that it had a highly accurate prediction. The MAPEs for conductivity, chloride, and total dissolved solids were between 10 and 20%, indicating that it had a good prediction. The MAPEs for ammonia, sulfate, total hardness, total organic carbon, iron, and manganese were between 20 and 50%, indicating that it had a reasonable prediction. But it had an inaccurate prediction for total alkalinity. Through the study, it is possible to develop an early warning tool for reducing the risk of water shortage during flood and drought due to climate change in the basin.
ISSN:0049-6979
1573-2932
DOI:10.1007/s11270-022-05931-z