Prediction of the groundwater quality index through machine learning in Western Middle Chelif plain in North Algeria

Elmeddahi, Yamina; Ragab, Ragab

doi:10.1007/s11600-022-00827-2

Artykuł - szczegóły

Tytuł artykułu

Prediction of the groundwater quality index through machine learning in Western Middle Chelif plain in North Algeria

Autorzy

Elmeddahi Yamina , Ragab Ragab

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-022-00827-2

Warianty tytułu

Języki publikacji

Abstrakty

Water quality monitoring and assessment has been one of the world’s major concerns in recent decades. This study examines the performance of three approaches based on the integration of machine learning and feature extraction techniques to improve water quality prediction in the Western Middle Chelif plain in Algeria during 2014–2018. The most dominant Water Quality Index parameters that were extracted by neuro-sensitivity analysis (NSA) and principal component analysis (PCA) techniques were used in the multilayer perceptron neural network, support vector regression (SVR) and decision tree regression models. Various combinations of input data were studied and evaluated in terms of prediction performance, using statistical criteria and graphical comparisons. According to the results, the MLPNN1 model with eight input parameters gave the highest performance for both training and validation phases (R=0.98/0.95, NSE=0.96/0.88, RMSE=11.20/15.03, MAE=7.89/10.22 and GA=1.34) when compared with the multiple linear regression, TDR and SVR models. Generally, the prediction performance of models integrated with NSA approaches is significantly improved and outperforms models coupled with the PCA dimensionality reduction method.

Słowa kluczowe

water resources machine learning SVR M LP DTR

zasoby wodne nauczanie maszynowe SVR M LP DTR

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2022

Tom

Vol. 70, no 4

Strony

1797--1814

Opis fizyczny

Bibliogr. 95 poz.

Twórcy

autor

Elmeddahi Yamina

y.elmeddahi@univ-chlef.dz

Department of Hydraulic, Civil Engineering and Architecture Faculty, University of Hassiba Benbouali, Chlef, Algeria
Vegetal Chemistry-Water-Energy Laboratory (LCV2E), Chlef, Algeria

https://orcid.org/0000-0002-2561-1524

autor

Ragab Ragab

Rag@ceh.ac.uk

UK Centre for Ecology & Hydrology, Maclean Building, Crowmarsh, Giford, Wallingford, Oxfordshire OX 10 8 BB, UK

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-34d73b40-3b37-475a-9f9c-7e7de527ef8c