干旱区生态环境知识资源中心(Arid): Water Quality Index Estimations Using Machine Learning Algorithms: A Case Study of Yazd-Ardakan Plain, Iran

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DOI	10.3390/w15101876
	Water Quality Index Estimations Using Machine Learning Algorithms: A Case Study of Yazd-Ardakan Plain, Iran
	Goodarzi, Mohammad Reza; Niknam, Amir Reza R.; Barzkar, Ali; Niazkar, Majid; Mehrjerdi, Yahia Zare; Abedi, Mohammad Javad; Pour, Mahnaz Heydari
通讯作者	Goodarzi, MR
来源期刊	WATER
EISSN	2073-4441
出版年	2023
卷号	15 期号:10
英文摘要	Excessive population growth and high water demands have significantly increased water extractions from deep and semi-deep wells in the arid regions of Iran. This has negatively affected water quality in different areas. The Water Quality Index (WQI) is a suitable tool to assess such impacts. This study used WQI and the fuzzy hierarchical analysis process of the water quality index (FAHP-WQI) to investigate the water quality status of 96 deep agricultural wells in the Yazd-Ardakan Plain, Iran. Calculating the WQI is time-consuming, but estimating WQI is inevitable for water resources management. For this purpose, three Machine Learning (ML) algorithms, namely, Gene Expression Programming (GEP), M5P Model tree, and Multivariate Adaptive Regression Splines (MARS), were employed to predict WQI. Using Wilcox and Schoeller charts, water quality was also investigated for agricultural and drinking purposes. The results demonstrated that 75% and 33% of the study area have good quality, based on the WQI and FAHP-WQI methods, respectively. According to the results of the Wilcox chart, around 37.25% of the wells are in the C3S2 and C3S1 classes, which indicate poor water quality. Schoeller's diagram placed the drinking water quality of the Yazd-Ardakan plain in acceptable, inadequate, and inappropriate categories. Afterwards, WQI, predicted by means of ML models, were compared on several statistical criteria. Finally, the comparative analysis revealed that MARS is slightly more accurate than the M5P model for estimating WQI.
英文关键词	water quality index machine learning fuzzy-AHP gene expression programming M5P MARS
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000997802100001
WOS关键词	GROUNDWATER QUALITY ; FUZZY ; DRINKING ; CHEMISTRY ; RIVER ; AREA ; MARS
WOS类目	Environmental Sciences ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/399028
推荐引用方式 GB/T 7714	Goodarzi, Mohammad Reza,Niknam, Amir Reza R.,Barzkar, Ali,et al. Water Quality Index Estimations Using Machine Learning Algorithms: A Case Study of Yazd-Ardakan Plain, Iran[J],2023,15(10).
APA	Goodarzi, Mohammad Reza.,Niknam, Amir Reza R..,Barzkar, Ali.,Niazkar, Majid.,Mehrjerdi, Yahia Zare.,...&Pour, Mahnaz Heydari.(2023).Water Quality Index Estimations Using Machine Learning Algorithms: A Case Study of Yazd-Ardakan Plain, Iran.WATER,15(10).
MLA	Goodarzi, Mohammad Reza,et al."Water Quality Index Estimations Using Machine Learning Algorithms: A Case Study of Yazd-Ardakan Plain, Iran".WATER 15.10(2023).