干旱区生态环境知识资源中心(Arid): Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios

Arid

DOI	10.1371/journal.pone.0251510
	Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios
	Shiri, Naser; Shiri, Jalal; Yaseen, Zaher Mundher; Kim, Sungwon; Chung, Il-Moon; Nourani, Vahid; Zounemat-Kermani, Mohammad
通讯作者	Yaseen, ZM (corresponding author), Univ Teknol Malaysia UTM, Sch Civil Engn, Fac Engn, Johor Baharu, Kagawa, Malaysia.
来源期刊	PLOS ONE
ISSN	1932-6203
出版年	2021
卷号	16 期号:5
英文摘要	Groundwater is one of the most important freshwater resources, especially in arid and semi-arid regions where the annual amounts of precipitation are small with frequent drought durations. Information on qualitative parameters of these valuable resources is very crucial as it might affect its applicability from agricultural, drinking, and industrial aspects. Although geo-statistics methods can provide insight about spatial distribution of quality factors, applications of advanced artificial intelligence (AI) models can contribute to produce more accurate results as robust alternative for such a complex geo-science problem. The present research investigates the capacity of several types of AI models for modeling four key water quality variables namely electrical conductivity (EC), sodium adsorption ratio (SAR), total dissolved solid (TDS) and Sulfate (SO4) using dataset obtained from 90 wells in Tabriz Plain, Iran; assessed by k-fold testing. Two different modeling scenarios were established to make simulations using other quality parameters and the geographical information. The obtained results confirmed the capabilities of the AI models for modeling the well groundwater quality variables. Among all the applied AI models, the developed hybrid support vector machine-firefly algorithm (SVM-FFA) model achieved the best predictability performance for both investigated scenarios. The introduced computer aid methodology provided a reliable technology for groundwater monitoring and assessment.
类型	Article
语种	英语
开放获取类型	gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:000664635700024
WOS关键词	NEURAL-NETWORK PREDICTION ; FLUORIDE CONTAMINATION ; WATER-RESOURCES ; RIVER-BASIN ; REGRESSION ; MACHINE ; SYSTEM ; PLAIN ; CLASSIFICATION ; INFORMATION
WOS类目	Multidisciplinary Sciences
WOS研究方向	Science & Technology - Other Topics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/364372
作者单位	[Shiri, Naser] Univ Tabriz, Fac Civil Engn, Tabriz, Iran; [Shiri, Jalal] Univ Tabriz, Water Engn Dept, Fac Agr, Tabriz, Iran; [Shiri, Jalal; Nourani, Vahid] Univ Tabriz, Ctr Excellence Hydroinformat, Fac Civil Engn, Tabriz, Iran; [Yaseen, Zaher Mundher] Univ Teknol Malaysia UTM, Sch Civil Engn, Fac Engn, Johor Baharu, Kagawa, Malaysia; [Kim, Sungwon] Dongyang Univ, Dept Railrd Construct & Safety Engn, Yeongju, South Korea; [Chung, Il-Moon] Korea Inst Civil Engn & Bldg Technol, Dept Land Water & Environm Res, Goyang, South Korea; [Nourani, Vahid] Near East Univ, Fac Civil & Environm Engn, Near East Blvd,Via Mersin 10, Nicosia, Turkey; [Zounemat-Kermani, Mohammad] Shahid Bahonar Univ Kerman, Water Engn Dept, Kerman, Iran
推荐引用方式 GB/T 7714	Shiri, Naser,Shiri, Jalal,Yaseen, Zaher Mundher,et al. Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios[J],2021,16(5).
APA	Shiri, Naser.,Shiri, Jalal.,Yaseen, Zaher Mundher.,Kim, Sungwon.,Chung, Il-Moon.,...&Zounemat-Kermani, Mohammad.(2021).Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios.PLOS ONE,16(5).
MLA	Shiri, Naser,et al."Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios".PLOS ONE 16.5(2021).