干旱区生态环境知识资源中心(Arid): How does a combination of numerical modeling, clustering, artificial intelligence, and evolutionary algorithms perform to predict regional groundwater levels?

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DOI	10.1016/j.compag.2022.107482
	How does a combination of numerical modeling, clustering, artificial intelligence, and evolutionary algorithms perform to predict regional groundwater levels?
	Kayhomayoon, Zahra; Ghordoyee-Milan, Sami; Jaafari, Abolfazl; Arya-Azar, Naser; Melesse, Assefa M.; Moghaddam, Hamid Kardan
通讯作者	Kayhomayoon, Z
来源期刊	COMPUTERS AND ELECTRONICS IN AGRICULTURE
ISSN	0168-1699
EISSN	1872-7107
出版年	2022
卷号	203
英文摘要	The prediction of groundwater levels in arid and semi-arid regions is of great importance to tailor the best water management strategies. In this study, we propose a new approach that combines simulation, clustering, and optimization tools for groundwater level prediction. This approach simulates groundwater levels (GWL) using the MODFLOW method, clusters the study aquifer into different clusters using the k-mean method, and predicts regional GWL using the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) methods that were optimized by the Harris Hawks Optimization (HHO), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO). The efficacy of our approach was evaluated via a case study in northwest Iran. The MODFLOW method simulated the distribution of GWL across the study area with R-2 = 0.99, root mean square error (RMSE) = 0.97 m (m) and mean absolute error (MAE) = 0.82 m. The k-means method clustered the aquifer into seven clusters based on the hydraulic conductivity, storage coefficient, groundwater level, groundwater depth, groundwater withdrawal, and aquifer saturation thickness parameters. The prediction of groundwater level for each cluster demonstrated the accurate performance of all optimized models with mean RMSE = 0.6 m and mean absolute percentage error (MAPE) = 0.23 m. The prediction phase identified groundwater level in the previous month (obtained from the MODFLOW method), withdrawal of aquifer, pre-cipitation, temperature, and evaporation as the most influential variables for groundwater levels in different clusters. We recommend the methodology proposed here for the prediction of groundwater levels in different aquifers with heterogeneous characteristics that pose computational burdens and uncertainties.
英文关键词	Artificial intelligence models Evolutionary algorithms Groundwater level Groundwater stability Spatial clustering
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000900087200005
WOS关键词	NEURAL-NETWORK ; FLUCTUATIONS ; SIMULATION ; ANFIS
WOS类目	Agriculture, Multidisciplinary ; Computer Science, Interdisciplinary Applications
WOS研究方向	Agriculture ; Computer Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/392186
推荐引用方式 GB/T 7714	Kayhomayoon, Zahra,Ghordoyee-Milan, Sami,Jaafari, Abolfazl,et al. How does a combination of numerical modeling, clustering, artificial intelligence, and evolutionary algorithms perform to predict regional groundwater levels?[J],2022,203.
APA	Kayhomayoon, Zahra,Ghordoyee-Milan, Sami,Jaafari, Abolfazl,Arya-Azar, Naser,Melesse, Assefa M.,&Moghaddam, Hamid Kardan.(2022).How does a combination of numerical modeling, clustering, artificial intelligence, and evolutionary algorithms perform to predict regional groundwater levels?.COMPUTERS AND ELECTRONICS IN AGRICULTURE,203.
MLA	Kayhomayoon, Zahra,et al."How does a combination of numerical modeling, clustering, artificial intelligence, and evolutionary algorithms perform to predict regional groundwater levels?".COMPUTERS AND ELECTRONICS IN AGRICULTURE 203(2022).