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Groundwater quality prediction based on LSTM RNN: An Iranian experience

Groundwater quality prediction has practical significance for the prevention of water pollution. Based on the exogenous variables which are effective on water quality indicators, this paper proposes a new method with new effective parameters based on LSTM RNN for groundwater quality index prediction...

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Detalles Bibliográficos
Autores principales: Valadkhan, D., Moghaddasi, R., Mohammadinejad, A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9255493/
https://www.ncbi.nlm.nih.gov/pubmed/35813581
http://dx.doi.org/10.1007/s13762-022-04356-9
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author Valadkhan, D.
Moghaddasi, R.
Mohammadinejad, A.
author_facet Valadkhan, D.
Moghaddasi, R.
Mohammadinejad, A.
author_sort Valadkhan, D.
collection PubMed
description Groundwater quality prediction has practical significance for the prevention of water pollution. Based on the exogenous variables which are effective on water quality indicators, this paper proposes a new method with new effective parameters based on LSTM RNN for groundwater quality index prediction. The effective parameters on the groundwater quality index include rainfall rate, temperature, and humidity, and groundwater abstraction was collected. Monthly time series data selection was done from five different locations in the Damavand region in Iran from 2009 to 2021. Neural network architecture is tested by “f-score” tested to obtain the best neural network performance. A comparison of the real value and the result of the prediction show that the water quality index prediction has been done sensibly and quite properly in most cases.
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spelling pubmed-92554932022-07-06 Groundwater quality prediction based on LSTM RNN: An Iranian experience Valadkhan, D. Moghaddasi, R. Mohammadinejad, A. Int J Environ Sci Technol (Tehran) Original Paper Groundwater quality prediction has practical significance for the prevention of water pollution. Based on the exogenous variables which are effective on water quality indicators, this paper proposes a new method with new effective parameters based on LSTM RNN for groundwater quality index prediction. The effective parameters on the groundwater quality index include rainfall rate, temperature, and humidity, and groundwater abstraction was collected. Monthly time series data selection was done from five different locations in the Damavand region in Iran from 2009 to 2021. Neural network architecture is tested by “f-score” tested to obtain the best neural network performance. A comparison of the real value and the result of the prediction show that the water quality index prediction has been done sensibly and quite properly in most cases. Springer Berlin Heidelberg 2022-07-05 2022 /pmc/articles/PMC9255493/ /pubmed/35813581 http://dx.doi.org/10.1007/s13762-022-04356-9 Text en © The Author(s) under exclusive licence to Iranian Society of Environmentalists (IRSEN) and Science and Research Branch, Islamic Azad University 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Paper
Valadkhan, D.
Moghaddasi, R.
Mohammadinejad, A.
Groundwater quality prediction based on LSTM RNN: An Iranian experience
title Groundwater quality prediction based on LSTM RNN: An Iranian experience
title_full Groundwater quality prediction based on LSTM RNN: An Iranian experience
title_fullStr Groundwater quality prediction based on LSTM RNN: An Iranian experience
title_full_unstemmed Groundwater quality prediction based on LSTM RNN: An Iranian experience
title_short Groundwater quality prediction based on LSTM RNN: An Iranian experience
title_sort groundwater quality prediction based on lstm rnn: an iranian experience
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9255493/
https://www.ncbi.nlm.nih.gov/pubmed/35813581
http://dx.doi.org/10.1007/s13762-022-04356-9
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AT moghaddasir groundwaterqualitypredictionbasedonlstmrnnaniranianexperience
AT mohammadinejada groundwaterqualitypredictionbasedonlstmrnnaniranianexperience