Modelling and Prediction of Water Quality by Using Artificial Intelligence

Artificial intelligence methods can remarkably reduce costs for water supply and sanitation systems and help ensure compliance with the quality of drinking and wastewater treatment. Therefore, modelling and predicting water quality to control water pollution has been widely researched. The novelty o...
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Autor*in:	Mosleh Hmoud Al-Adhaileh [verfasserIn] Fawaz Waselallah Alsaade [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	water quality water quality index water quality classification adaptive neuro-fuzzy inference system feed-forward neural network models

Übergeordnetes Werk:	In: Sustainability - MDPI AG, 2009, 13(2021), 8, p 4259
Übergeordnetes Werk:	volume:13 ; year:2021 ; number:8, p 4259

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/su13084259

Katalog-ID:	DOAJ069324735

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allfieldsGer	10.3390/su13084259 doi (DE-627)DOAJ069324735 (DE-599)DOAJ866b7e7ce1ac413d9adfe63e49266351 DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Mosleh Hmoud Al-Adhaileh verfasserin aut Modelling and Prediction of Water Quality by Using Artificial Intelligence 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Artificial intelligence methods can remarkably reduce costs for water supply and sanitation systems and help ensure compliance with the quality of drinking and wastewater treatment. Therefore, modelling and predicting water quality to control water pollution has been widely researched. The novelty of the proposed system is presented to develop an efficient operation of monitoring drinking water to ensure a sustainable and friendly green environment. In this work, the adaptive neuro-fuzzy inference system (ANFIS) algorithm was developed to predict the water quality index (WQI). Feed-forward neural network (FFNN) and K-nearest neighbors were applied to classify water quality. The dataset has eight significant parameters, but seven parameters were considered to show significant values. The proposed methodology was developed based on these statistical parameters. Prediction results demonstrated that the ANFIS model was superior for the prediction of WQI values. Nevertheless, the FFNN algorithm achieved the highest accuracy (100%) for water quality classification (WQC). Furthermore, the ANFIS model accurately predicted WQI, and the FFNN model showed superior robustness in classifying the WQC. In addition, the ANFIS model showed accuracy during the testing phase, with a regression coefficient of 96.17% for predicting WQI, and the FFNN model achieved the highest accuracy (100%) for WQC. This proposed method, using advanced artificial intelligence, can aid in water treatment and management. water quality water quality index water quality classification adaptive neuro-fuzzy inference system feed-forward neural network models Environmental effects of industries and plants Renewable energy sources Environmental sciences Fawaz Waselallah Alsaade verfasserin aut In Sustainability MDPI AG, 2009 13(2021), 8, p 4259 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:13 year:2021 number:8, p 4259 https://doi.org/10.3390/su13084259 kostenfrei https://doaj.org/article/866b7e7ce1ac413d9adfe63e49266351 kostenfrei https://www.mdpi.com/2071-1050/13/8/4259 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_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_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 8, p 4259
allfieldsSound	10.3390/su13084259 doi (DE-627)DOAJ069324735 (DE-599)DOAJ866b7e7ce1ac413d9adfe63e49266351 DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Mosleh Hmoud Al-Adhaileh verfasserin aut Modelling and Prediction of Water Quality by Using Artificial Intelligence 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Artificial intelligence methods can remarkably reduce costs for water supply and sanitation systems and help ensure compliance with the quality of drinking and wastewater treatment. Therefore, modelling and predicting water quality to control water pollution has been widely researched. The novelty of the proposed system is presented to develop an efficient operation of monitoring drinking water to ensure a sustainable and friendly green environment. In this work, the adaptive neuro-fuzzy inference system (ANFIS) algorithm was developed to predict the water quality index (WQI). Feed-forward neural network (FFNN) and K-nearest neighbors were applied to classify water quality. The dataset has eight significant parameters, but seven parameters were considered to show significant values. The proposed methodology was developed based on these statistical parameters. Prediction results demonstrated that the ANFIS model was superior for the prediction of WQI values. Nevertheless, the FFNN algorithm achieved the highest accuracy (100%) for water quality classification (WQC). Furthermore, the ANFIS model accurately predicted WQI, and the FFNN model showed superior robustness in classifying the WQC. In addition, the ANFIS model showed accuracy during the testing phase, with a regression coefficient of 96.17% for predicting WQI, and the FFNN model achieved the highest accuracy (100%) for WQC. This proposed method, using advanced artificial intelligence, can aid in water treatment and management. water quality water quality index water quality classification adaptive neuro-fuzzy inference system feed-forward neural network models Environmental effects of industries and plants Renewable energy sources Environmental sciences Fawaz Waselallah Alsaade verfasserin aut In Sustainability MDPI AG, 2009 13(2021), 8, p 4259 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:13 year:2021 number:8, p 4259 https://doi.org/10.3390/su13084259 kostenfrei https://doaj.org/article/866b7e7ce1ac413d9adfe63e49266351 kostenfrei https://www.mdpi.com/2071-1050/13/8/4259 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_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_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 8, p 4259
language	English
source	In Sustainability 13(2021), 8, p 4259 volume:13 year:2021 number:8, p 4259
sourceStr	In Sustainability 13(2021), 8, p 4259 volume:13 year:2021 number:8, p 4259
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topic_facet	water quality water quality index water quality classification adaptive neuro-fuzzy inference system feed-forward neural network models Environmental effects of industries and plants Renewable energy sources Environmental sciences
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container_title	Sustainability
authorswithroles_txt_mv	Mosleh Hmoud Al-Adhaileh @@aut@@ Fawaz Waselallah Alsaade @@aut@@
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callnumber-first	T - Technology
author	Mosleh Hmoud Al-Adhaileh
spellingShingle	Mosleh Hmoud Al-Adhaileh misc TD194-195 misc TJ807-830 misc GE1-350 misc water quality misc water quality index misc water quality classification misc adaptive neuro-fuzzy inference system misc feed-forward neural network models misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences Modelling and Prediction of Water Quality by Using Artificial Intelligence
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topic_title	TD194-195 TJ807-830 GE1-350 Modelling and Prediction of Water Quality by Using Artificial Intelligence water quality water quality index water quality classification adaptive neuro-fuzzy inference system feed-forward neural network models
topic	misc TD194-195 misc TJ807-830 misc GE1-350 misc water quality misc water quality index misc water quality classification misc adaptive neuro-fuzzy inference system misc feed-forward neural network models misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
topic_unstemmed	misc TD194-195 misc TJ807-830 misc GE1-350 misc water quality misc water quality index misc water quality classification misc adaptive neuro-fuzzy inference system misc feed-forward neural network models misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
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title	Modelling and Prediction of Water Quality by Using Artificial Intelligence
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title_full	Modelling and Prediction of Water Quality by Using Artificial Intelligence
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title_sort	modelling and prediction of water quality by using artificial intelligence
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title_auth	Modelling and Prediction of Water Quality by Using Artificial Intelligence
abstract	Artificial intelligence methods can remarkably reduce costs for water supply and sanitation systems and help ensure compliance with the quality of drinking and wastewater treatment. Therefore, modelling and predicting water quality to control water pollution has been widely researched. The novelty of the proposed system is presented to develop an efficient operation of monitoring drinking water to ensure a sustainable and friendly green environment. In this work, the adaptive neuro-fuzzy inference system (ANFIS) algorithm was developed to predict the water quality index (WQI). Feed-forward neural network (FFNN) and K-nearest neighbors were applied to classify water quality. The dataset has eight significant parameters, but seven parameters were considered to show significant values. The proposed methodology was developed based on these statistical parameters. Prediction results demonstrated that the ANFIS model was superior for the prediction of WQI values. Nevertheless, the FFNN algorithm achieved the highest accuracy (100%) for water quality classification (WQC). Furthermore, the ANFIS model accurately predicted WQI, and the FFNN model showed superior robustness in classifying the WQC. In addition, the ANFIS model showed accuracy during the testing phase, with a regression coefficient of 96.17% for predicting WQI, and the FFNN model achieved the highest accuracy (100%) for WQC. This proposed method, using advanced artificial intelligence, can aid in water treatment and management.
abstractGer	Artificial intelligence methods can remarkably reduce costs for water supply and sanitation systems and help ensure compliance with the quality of drinking and wastewater treatment. Therefore, modelling and predicting water quality to control water pollution has been widely researched. The novelty of the proposed system is presented to develop an efficient operation of monitoring drinking water to ensure a sustainable and friendly green environment. In this work, the adaptive neuro-fuzzy inference system (ANFIS) algorithm was developed to predict the water quality index (WQI). Feed-forward neural network (FFNN) and K-nearest neighbors were applied to classify water quality. The dataset has eight significant parameters, but seven parameters were considered to show significant values. The proposed methodology was developed based on these statistical parameters. Prediction results demonstrated that the ANFIS model was superior for the prediction of WQI values. Nevertheless, the FFNN algorithm achieved the highest accuracy (100%) for water quality classification (WQC). Furthermore, the ANFIS model accurately predicted WQI, and the FFNN model showed superior robustness in classifying the WQC. In addition, the ANFIS model showed accuracy during the testing phase, with a regression coefficient of 96.17% for predicting WQI, and the FFNN model achieved the highest accuracy (100%) for WQC. This proposed method, using advanced artificial intelligence, can aid in water treatment and management.
abstract_unstemmed	Artificial intelligence methods can remarkably reduce costs for water supply and sanitation systems and help ensure compliance with the quality of drinking and wastewater treatment. Therefore, modelling and predicting water quality to control water pollution has been widely researched. The novelty of the proposed system is presented to develop an efficient operation of monitoring drinking water to ensure a sustainable and friendly green environment. In this work, the adaptive neuro-fuzzy inference system (ANFIS) algorithm was developed to predict the water quality index (WQI). Feed-forward neural network (FFNN) and K-nearest neighbors were applied to classify water quality. The dataset has eight significant parameters, but seven parameters were considered to show significant values. The proposed methodology was developed based on these statistical parameters. Prediction results demonstrated that the ANFIS model was superior for the prediction of WQI values. Nevertheless, the FFNN algorithm achieved the highest accuracy (100%) for water quality classification (WQC). Furthermore, the ANFIS model accurately predicted WQI, and the FFNN model showed superior robustness in classifying the WQC. In addition, the ANFIS model showed accuracy during the testing phase, with a regression coefficient of 96.17% for predicting WQI, and the FFNN model achieved the highest accuracy (100%) for WQC. This proposed method, using advanced artificial intelligence, can aid in water treatment and management.
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title_short	Modelling and Prediction of Water Quality by Using Artificial Intelligence
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