干旱区生态环境知识资源中心(Arid): Nitrate concentration analysis and prediction in a shallow aquifer in central-eastern Tunisia using artificial neural network and time series modelling

Arid

DOI	10.1007/s11356-021-18174-y
	Nitrate concentration analysis and prediction in a shallow aquifer in central-eastern Tunisia using artificial neural network and time series modelling
	El Amri, Asma; M'nassri, Soumaia; Nasri, Nessrine; Nsir, Hanen; Majdoub, Rajouene
通讯作者	M'nassri, S
来源期刊	ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
ISSN	0944-1344
EISSN	1614-7499
出版年	2022
英文摘要	Agricultural activities have become a major source of groundwater nitrate contamination. In this context, this study aims to analyse nitrate concentrations in a shallow aquifer of Mahdia-Kssour Essef in central-eastern Tunisia, identify the assignable sources, and predict the future levels using artificial neural network (ANN) and autoregressive integrated moving average (ARIMA) models. The results showed that nitrate concentrations measured in 21 pumping wells across the plain ranged from 17 to 521 mg L-1. A total of 67% of the monitoring points greatly exceed the standard guideline value of 50 mg L-1. The main relevant anthropogenic and natural factors, such as soil texture, land use, fertilizers application rates, livestock waste disposal, and groundwater table, are positively correlated with groundwater nitrate concentration. The ANN model showed good fitting between measured and simulated results with coefficient of determination (R-2), root-mean-square error (RMSE), and mean absolute error (MAE) values of 0.88, 53.95, and 39.64, respectively. The ARIMA applied on annual average nitrate concentrations from 1998 to 2017 revealed that the best fitted model (p, d, q) is (1, 2, 1). The R-2 value is approximately 0.36, and the Theil inequality coefficient and bias proportion values are small and close to zero. These results proved the ARIMA model's adequacy in forecasting annual average nitrate concentrations of 116 mg L-1 in 2025. These findings may be useful in making groundwater management decisions, particularly in rural and semi-arid areas, and the proposed ARIMA model could be used as a managed tool to monitor and reduce the nitrate intrusion into groundwater.
英文关键词	Nitrate pollution Groundwater quality Artificial neural network ARIMA Time series Tunisia
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000749137700003
WOS关键词	WATER-QUALITY ; GROUNDWATER VULNERABILITY ; COASTAL AQUIFER ; RISK-ASSESSMENT ; SOIL ; CONTAMINATION ; POLLUTION ; PERFORMANCE ; TRANSPORT ; ARIMA
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/377065
推荐引用方式 GB/T 7714	El Amri, Asma,M'nassri, Soumaia,Nasri, Nessrine,et al. Nitrate concentration analysis and prediction in a shallow aquifer in central-eastern Tunisia using artificial neural network and time series modelling[J],2022.
APA	El Amri, Asma,M'nassri, Soumaia,Nasri, Nessrine,Nsir, Hanen,&Majdoub, Rajouene.(2022).Nitrate concentration analysis and prediction in a shallow aquifer in central-eastern Tunisia using artificial neural network and time series modelling.ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH.
MLA	El Amri, Asma,et al."Nitrate concentration analysis and prediction in a shallow aquifer in central-eastern Tunisia using artificial neural network and time series modelling".ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2022).