Prediction of groundwater flow in shallow aquifers using artificial neural networks in the northern basins of Algeria

Prediction of groundwater flow fluctuations is considered an important step in understanding groundwater systems at this scale and facilitating sustainable groundwater management. The objective of this study is to determine the factors that influence and control groundwater flow fluctuations in a sp...

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Autores principales:	N. Guezgouz, D. Boutoutaou, A. Hani
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	groundwater limited data model northern algeria prediction Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/254d8cacef714cd4bb329504e97f2500
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spelling	oai:doaj.org-article:254d8cacef714cd4bb329504e97f25002021-11-05T18:52:29ZPrediction of groundwater flow in shallow aquifers using artificial neural networks in the northern basins of Algeria2040-22442408-935410.2166/wcc.2020.067https://doaj.org/article/254d8cacef714cd4bb329504e97f25002021-06-01T00:00:00Zhttp://jwcc.iwaponline.com/content/12/4/1220https://doaj.org/toc/2040-2244https://doaj.org/toc/2408-9354Prediction of groundwater flow fluctuations is considered an important step in understanding groundwater systems at this scale and facilitating sustainable groundwater management. The objective of this study is to determine the factors that influence and control groundwater flow fluctuations in a specific geomorphologic situation, by developing a forecasting model and examining its potential for predicting groundwater flow using limited data. Models for prediction of groundwater flow are developed based on artificial neural networks (ANNs). Neural networks with different numbers of hidden layer neurons were developed using climatic and geomorphological characteristics as input variables, giving predicted groundwater flow as the output. To evaluate enhanced performance models, several regression statistical parameters are compared. As an example, relative mean square error in groundwater flow prediction by ANN and correlation coefficient are 0.015 and 97%, respectively. The results of the study clearly show that ANNs can be used to predict groundwater flow in shallow aquifers of northern Algeria with reasonable accuracy even in the case of limited data. HIGHLIGHTS Combine hydrological and climatic data to estimate groundwater flows.; Test the performance of ANN's models to understand the behavior of groundwater.; Large-scale groundwater flow modeling for better management of water resources.; Proposal of a predictive model for a global vision of the distribution of groundwater.; Determining the order of importance of indicators that can influence groundwater flows.;N. GuezgouzD. BoutoutaouA. HaniIWA Publishingarticlegroundwaterlimited datamodelnorthern algeriapredictionEnvironmental technology. Sanitary engineeringTD1-1066Environmental sciencesGE1-350ENJournal of Water and Climate Change, Vol 12, Iss 4, Pp 1220-1228 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	groundwater limited data model northern algeria prediction Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
spellingShingle	groundwater limited data model northern algeria prediction Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 N. Guezgouz D. Boutoutaou A. Hani Prediction of groundwater flow in shallow aquifers using artificial neural networks in the northern basins of Algeria
description	Prediction of groundwater flow fluctuations is considered an important step in understanding groundwater systems at this scale and facilitating sustainable groundwater management. The objective of this study is to determine the factors that influence and control groundwater flow fluctuations in a specific geomorphologic situation, by developing a forecasting model and examining its potential for predicting groundwater flow using limited data. Models for prediction of groundwater flow are developed based on artificial neural networks (ANNs). Neural networks with different numbers of hidden layer neurons were developed using climatic and geomorphological characteristics as input variables, giving predicted groundwater flow as the output. To evaluate enhanced performance models, several regression statistical parameters are compared. As an example, relative mean square error in groundwater flow prediction by ANN and correlation coefficient are 0.015 and 97%, respectively. The results of the study clearly show that ANNs can be used to predict groundwater flow in shallow aquifers of northern Algeria with reasonable accuracy even in the case of limited data. HIGHLIGHTS Combine hydrological and climatic data to estimate groundwater flows.; Test the performance of ANN's models to understand the behavior of groundwater.; Large-scale groundwater flow modeling for better management of water resources.; Proposal of a predictive model for a global vision of the distribution of groundwater.; Determining the order of importance of indicators that can influence groundwater flows.;
format	article
author	N. Guezgouz D. Boutoutaou A. Hani
author_facet	N. Guezgouz D. Boutoutaou A. Hani
author_sort	N. Guezgouz
title	Prediction of groundwater flow in shallow aquifers using artificial neural networks in the northern basins of Algeria
title_short	Prediction of groundwater flow in shallow aquifers using artificial neural networks in the northern basins of Algeria
title_full	Prediction of groundwater flow in shallow aquifers using artificial neural networks in the northern basins of Algeria
title_fullStr	Prediction of groundwater flow in shallow aquifers using artificial neural networks in the northern basins of Algeria
title_full_unstemmed	Prediction of groundwater flow in shallow aquifers using artificial neural networks in the northern basins of Algeria
title_sort	prediction of groundwater flow in shallow aquifers using artificial neural networks in the northern basins of algeria
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/254d8cacef714cd4bb329504e97f2500
work_keys_str_mv	AT nguezgouz predictionofgroundwaterflowinshallowaquifersusingartificialneuralnetworksinthenorthernbasinsofalgeria AT dboutoutaou predictionofgroundwaterflowinshallowaquifersusingartificialneuralnetworksinthenorthernbasinsofalgeria AT ahani predictionofgroundwaterflowinshallowaquifersusingartificialneuralnetworksinthenorthernbasinsofalgeria
_version_	1718444089474547712

Prediction of groundwater flow in shallow aquifers using artificial neural networks in the northern basins of Algeria

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