Bidirectional Long Short-Term Memory (BILSTM) - Support Vector Machine: A new machine learning model for predicting water quality parameters

Water pollution threatens human health, agriculture, and ecosystems. Accurate prediction of water quality parameters is crucial for effective protection. We suggest a novel hybrid deep learning model that enhances the efficiency of Support Vector Machines (SVMs) in predicting Electrical Conductivity...
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Autor*in:	Zahra Jamshidzadeh [verfasserIn] Mohammad Ehteram [verfasserIn] Hanieh Shabanian [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Water quality Deep learning Optimization Total dissolved solids Bidirectional LSTM

Übergeordnetes Werk:	In: Ain Shams Engineering Journal - Elsevier, 2016, 15(2024), 3, Seite 102510-
Übergeordnetes Werk:	volume:15 ; year:2024 ; number:3 ; pages:102510-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.asej.2023.102510

Katalog-ID:	DOAJ099748878

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allfieldsGer	10.1016/j.asej.2023.102510 doi (DE-627)DOAJ099748878 (DE-599)DOAJ79cb6928c11741b49b63c20bfb90ad4c DE-627 ger DE-627 rakwb eng TA1-2040 Zahra Jamshidzadeh verfasserin aut Bidirectional Long Short-Term Memory (BILSTM) - Support Vector Machine: A new machine learning model for predicting water quality parameters 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Water pollution threatens human health, agriculture, and ecosystems. Accurate prediction of water quality parameters is crucial for effective protection. We suggest a novel hybrid deep learning model that enhances the efficiency of Support Vector Machines (SVMs) in predicting Electrical Conductivity (EC) and Total Dissolved Solids (TDS). Our model combines Bidirectional Long Short-Term Memory (BILSTM) and SVMs to extract essential features and predict output variables. We evaluated the models using input parameters (PH, Ca++, Mg++, Na+, K+, HCO3, SO4, and Cl) for one, two, and three-day predictions. Employing the Ali Baba and Forty Thieves (AFT) optimization algorithm, we identified optimal input combinations. The BILSTM-SVM model accurately estimated TDS values, with MAPE values of 2%, outperforming other models. Similarly, it successfully predicted EC values, exhibiting an R2 value of 0.94. Our proposed model processes complex relationships and captures crucial features from the data, contributing to improved water quality prediction. Water quality Deep learning Optimization Total dissolved solids Bidirectional LSTM Engineering (General). Civil engineering (General) Mohammad Ehteram verfasserin aut Hanieh Shabanian verfasserin aut In Ain Shams Engineering Journal Elsevier, 2016 15(2024), 3, Seite 102510- (DE-627)642886237 (DE-600)2586876-7 20904495 nnns volume:15 year:2024 number:3 pages:102510- https://doi.org/10.1016/j.asej.2023.102510 kostenfrei https://doaj.org/article/79cb6928c11741b49b63c20bfb90ad4c kostenfrei http://www.sciencedirect.com/science/article/pii/S2090447923003994 kostenfrei https://doaj.org/toc/2090-4479 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 15 2024 3 102510-
allfieldsSound	10.1016/j.asej.2023.102510 doi (DE-627)DOAJ099748878 (DE-599)DOAJ79cb6928c11741b49b63c20bfb90ad4c DE-627 ger DE-627 rakwb eng TA1-2040 Zahra Jamshidzadeh verfasserin aut Bidirectional Long Short-Term Memory (BILSTM) - Support Vector Machine: A new machine learning model for predicting water quality parameters 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Water pollution threatens human health, agriculture, and ecosystems. Accurate prediction of water quality parameters is crucial for effective protection. We suggest a novel hybrid deep learning model that enhances the efficiency of Support Vector Machines (SVMs) in predicting Electrical Conductivity (EC) and Total Dissolved Solids (TDS). Our model combines Bidirectional Long Short-Term Memory (BILSTM) and SVMs to extract essential features and predict output variables. We evaluated the models using input parameters (PH, Ca++, Mg++, Na+, K+, HCO3, SO4, and Cl) for one, two, and three-day predictions. Employing the Ali Baba and Forty Thieves (AFT) optimization algorithm, we identified optimal input combinations. The BILSTM-SVM model accurately estimated TDS values, with MAPE values of 2%, outperforming other models. Similarly, it successfully predicted EC values, exhibiting an R2 value of 0.94. Our proposed model processes complex relationships and captures crucial features from the data, contributing to improved water quality prediction. Water quality Deep learning Optimization Total dissolved solids Bidirectional LSTM Engineering (General). Civil engineering (General) Mohammad Ehteram verfasserin aut Hanieh Shabanian verfasserin aut In Ain Shams Engineering Journal Elsevier, 2016 15(2024), 3, Seite 102510- (DE-627)642886237 (DE-600)2586876-7 20904495 nnns volume:15 year:2024 number:3 pages:102510- https://doi.org/10.1016/j.asej.2023.102510 kostenfrei https://doaj.org/article/79cb6928c11741b49b63c20bfb90ad4c kostenfrei http://www.sciencedirect.com/science/article/pii/S2090447923003994 kostenfrei https://doaj.org/toc/2090-4479 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 15 2024 3 102510-
language	English
source	In Ain Shams Engineering Journal 15(2024), 3, Seite 102510- volume:15 year:2024 number:3 pages:102510-
sourceStr	In Ain Shams Engineering Journal 15(2024), 3, Seite 102510- volume:15 year:2024 number:3 pages:102510-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Water quality Deep learning Optimization Total dissolved solids Bidirectional LSTM Engineering (General). Civil engineering (General)
isfreeaccess_bool	true
container_title	Ain Shams Engineering Journal
authorswithroles_txt_mv	Zahra Jamshidzadeh @@aut@@ Mohammad Ehteram @@aut@@ Hanieh Shabanian @@aut@@
publishDateDaySort_date	2024-01-01T00:00:00Z
hierarchy_top_id	642886237
id	DOAJ099748878
language_de	englisch
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callnumber-first	T - Technology
author	Zahra Jamshidzadeh
spellingShingle	Zahra Jamshidzadeh misc TA1-2040 misc Water quality misc Deep learning misc Optimization misc Total dissolved solids misc Bidirectional LSTM misc Engineering (General). Civil engineering (General) Bidirectional Long Short-Term Memory (BILSTM) - Support Vector Machine: A new machine learning model for predicting water quality parameters
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topic_title	TA1-2040 Bidirectional Long Short-Term Memory (BILSTM) - Support Vector Machine: A new machine learning model for predicting water quality parameters Water quality Deep learning Optimization Total dissolved solids Bidirectional LSTM
topic	misc TA1-2040 misc Water quality misc Deep learning misc Optimization misc Total dissolved solids misc Bidirectional LSTM misc Engineering (General). Civil engineering (General)
topic_unstemmed	misc TA1-2040 misc Water quality misc Deep learning misc Optimization misc Total dissolved solids misc Bidirectional LSTM misc Engineering (General). Civil engineering (General)
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title	Bidirectional Long Short-Term Memory (BILSTM) - Support Vector Machine: A new machine learning model for predicting water quality parameters
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title_full	Bidirectional Long Short-Term Memory (BILSTM) - Support Vector Machine: A new machine learning model for predicting water quality parameters
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title_sort	bidirectional long short-term memory (bilstm) - support vector machine: a new machine learning model for predicting water quality parameters
callnumber	TA1-2040
title_auth	Bidirectional Long Short-Term Memory (BILSTM) - Support Vector Machine: A new machine learning model for predicting water quality parameters
abstract	Water pollution threatens human health, agriculture, and ecosystems. Accurate prediction of water quality parameters is crucial for effective protection. We suggest a novel hybrid deep learning model that enhances the efficiency of Support Vector Machines (SVMs) in predicting Electrical Conductivity (EC) and Total Dissolved Solids (TDS). Our model combines Bidirectional Long Short-Term Memory (BILSTM) and SVMs to extract essential features and predict output variables. We evaluated the models using input parameters (PH, Ca++, Mg++, Na+, K+, HCO3, SO4, and Cl) for one, two, and three-day predictions. Employing the Ali Baba and Forty Thieves (AFT) optimization algorithm, we identified optimal input combinations. The BILSTM-SVM model accurately estimated TDS values, with MAPE values of 2%, outperforming other models. Similarly, it successfully predicted EC values, exhibiting an R2 value of 0.94. Our proposed model processes complex relationships and captures crucial features from the data, contributing to improved water quality prediction.
abstractGer	Water pollution threatens human health, agriculture, and ecosystems. Accurate prediction of water quality parameters is crucial for effective protection. We suggest a novel hybrid deep learning model that enhances the efficiency of Support Vector Machines (SVMs) in predicting Electrical Conductivity (EC) and Total Dissolved Solids (TDS). Our model combines Bidirectional Long Short-Term Memory (BILSTM) and SVMs to extract essential features and predict output variables. We evaluated the models using input parameters (PH, Ca++, Mg++, Na+, K+, HCO3, SO4, and Cl) for one, two, and three-day predictions. Employing the Ali Baba and Forty Thieves (AFT) optimization algorithm, we identified optimal input combinations. The BILSTM-SVM model accurately estimated TDS values, with MAPE values of 2%, outperforming other models. Similarly, it successfully predicted EC values, exhibiting an R2 value of 0.94. Our proposed model processes complex relationships and captures crucial features from the data, contributing to improved water quality prediction.
abstract_unstemmed	Water pollution threatens human health, agriculture, and ecosystems. Accurate prediction of water quality parameters is crucial for effective protection. We suggest a novel hybrid deep learning model that enhances the efficiency of Support Vector Machines (SVMs) in predicting Electrical Conductivity (EC) and Total Dissolved Solids (TDS). Our model combines Bidirectional Long Short-Term Memory (BILSTM) and SVMs to extract essential features and predict output variables. We evaluated the models using input parameters (PH, Ca++, Mg++, Na+, K+, HCO3, SO4, and Cl) for one, two, and three-day predictions. Employing the Ali Baba and Forty Thieves (AFT) optimization algorithm, we identified optimal input combinations. The BILSTM-SVM model accurately estimated TDS values, with MAPE values of 2%, outperforming other models. Similarly, it successfully predicted EC values, exhibiting an R2 value of 0.94. Our proposed model processes complex relationships and captures crucial features from the data, contributing to improved water quality prediction.
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title_short	Bidirectional Long Short-Term Memory (BILSTM) - Support Vector Machine: A new machine learning model for predicting water quality parameters
url	https://doi.org/10.1016/j.asej.2023.102510 https://doaj.org/article/79cb6928c11741b49b63c20bfb90ad4c http://www.sciencedirect.com/science/article/pii/S2090447923003994 https://doaj.org/toc/2090-4479
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up_date	2024-07-04T00:11:00.495Z
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