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Performance evaluation of hydroponic wastewater treatment plant integrated with ensemble learning techniques : A feature selection approach

Wastewater treatment and reuse are being regarded as the most effective strategy for combating water scarcity threats. This study examined and reported the applications of the Internet of Things (IoT) and artificial intelligence in the phytoremediation of wastewater using Salvinia molesta plants. Wa...

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Main Authors: Mustafa, Hauwa Mohammed, Hayder, Gasim, Abba, S.I., Algarni, Abeer D., Mnzool, Mohammed, Nour, Abdurahman H.
Format: Article
Language:English
Published: MDPI 2023
Subjects:
QD Chemistry
T Technology (General)
TA Engineering (General). Civil engineering (General)
TP Chemical technology
Online Access:http://umpir.ump.edu.my/id/eprint/37639/1/Performance%20evaluation%20of%20hydroponic%20wastewater%20treatment%20plant%20integrated.pdf
http://umpir.ump.edu.my/id/eprint/37639/
https://doi.org/10.3390/pr11020478
https://doi.org/10.3390/pr11020478
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spelling my.ump.umpir.376392023-07-14T02:34:05Z http://umpir.ump.edu.my/id/eprint/37639/ Performance evaluation of hydroponic wastewater treatment plant integrated with ensemble learning techniques : A feature selection approach Mustafa, Hauwa Mohammed Hayder, Gasim Abba, S.I. Algarni, Abeer D. Mnzool, Mohammed Nour, Abdurahman H. QD Chemistry T Technology (General) TA Engineering (General). Civil engineering (General) TP Chemical technology Wastewater treatment and reuse are being regarded as the most effective strategy for combating water scarcity threats. This study examined and reported the applications of the Internet of Things (IoT) and artificial intelligence in the phytoremediation of wastewater using Salvinia molesta plants. Water quality (WQ) indicators (total dissolved solids (TDS), temperature, oxidation-reduction potential (ORP), and turbidity) of the S. molesta treatment system at a retention time of 24 h were measured using an Arduino IoT device. Finally, four machine learning tools (ML) were employed in modeling and evaluating the predicted concentration of the total dissolved solids after treatment (TDSt) of the water samples. Additionally, three nonlinear error ensemble methods were used to enhance the prediction accuracy of the TDSt models. The outcome obtained from the modeling and prediction of the TDSt depicted that the best results were observed at SVM-M1 with 0.9999, 0.0139, 1.0000, and 0.1177 for R2, MSE, R, and RMSE, respectively, at the training stage. While at the validation stage, the R2, MSE, R, and RMSE were recorded as 0.9986, 0.0356, 0.993, and 0.1887, respectively. Furthermore, the error ensemble techniques employed significantly outperformed the single models in terms of mean square error (MSE) and root mean square error (RMSE) for both training and validation, with 0.0014 and 0.0379, respectively. MDPI 2023-02 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/37639/1/Performance%20evaluation%20of%20hydroponic%20wastewater%20treatment%20plant%20integrated.pdf Mustafa, Hauwa Mohammed and Hayder, Gasim and Abba, S.I. and Algarni, Abeer D. and Mnzool, Mohammed and Nour, Abdurahman H. (2023) Performance evaluation of hydroponic wastewater treatment plant integrated with ensemble learning techniques : A feature selection approach. Processes, 11 (478). pp. 1-16. ISSN 2227-9717. (Published) https://doi.org/10.3390/pr11020478 https://doi.org/10.3390/pr11020478
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QD Chemistry
T Technology (General)
TA Engineering (General). Civil engineering (General)
TP Chemical technology
spellingShingle QD Chemistry
T Technology (General)
TA Engineering (General). Civil engineering (General)
TP Chemical technology
Mustafa, Hauwa Mohammed
Hayder, Gasim
Abba, S.I.
Algarni, Abeer D.
Mnzool, Mohammed
Nour, Abdurahman H.
Performance evaluation of hydroponic wastewater treatment plant integrated with ensemble learning techniques : A feature selection approach
description Wastewater treatment and reuse are being regarded as the most effective strategy for combating water scarcity threats. This study examined and reported the applications of the Internet of Things (IoT) and artificial intelligence in the phytoremediation of wastewater using Salvinia molesta plants. Water quality (WQ) indicators (total dissolved solids (TDS), temperature, oxidation-reduction potential (ORP), and turbidity) of the S. molesta treatment system at a retention time of 24 h were measured using an Arduino IoT device. Finally, four machine learning tools (ML) were employed in modeling and evaluating the predicted concentration of the total dissolved solids after treatment (TDSt) of the water samples. Additionally, three nonlinear error ensemble methods were used to enhance the prediction accuracy of the TDSt models. The outcome obtained from the modeling and prediction of the TDSt depicted that the best results were observed at SVM-M1 with 0.9999, 0.0139, 1.0000, and 0.1177 for R2, MSE, R, and RMSE, respectively, at the training stage. While at the validation stage, the R2, MSE, R, and RMSE were recorded as 0.9986, 0.0356, 0.993, and 0.1887, respectively. Furthermore, the error ensemble techniques employed significantly outperformed the single models in terms of mean square error (MSE) and root mean square error (RMSE) for both training and validation, with 0.0014 and 0.0379, respectively.
format Article
author Mustafa, Hauwa Mohammed
Hayder, Gasim
Abba, S.I.
Algarni, Abeer D.
Mnzool, Mohammed
Nour, Abdurahman H.
author_facet Mustafa, Hauwa Mohammed
Hayder, Gasim
Abba, S.I.
Algarni, Abeer D.
Mnzool, Mohammed
Nour, Abdurahman H.
author_sort Mustafa, Hauwa Mohammed
title Performance evaluation of hydroponic wastewater treatment plant integrated with ensemble learning techniques : A feature selection approach
title_short Performance evaluation of hydroponic wastewater treatment plant integrated with ensemble learning techniques : A feature selection approach
title_full Performance evaluation of hydroponic wastewater treatment plant integrated with ensemble learning techniques : A feature selection approach
title_fullStr Performance evaluation of hydroponic wastewater treatment plant integrated with ensemble learning techniques : A feature selection approach
title_full_unstemmed Performance evaluation of hydroponic wastewater treatment plant integrated with ensemble learning techniques : A feature selection approach
title_sort performance evaluation of hydroponic wastewater treatment plant integrated with ensemble learning techniques : a feature selection approach
publisher MDPI
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/37639/1/Performance%20evaluation%20of%20hydroponic%20wastewater%20treatment%20plant%20integrated.pdf
http://umpir.ump.edu.my/id/eprint/37639/
https://doi.org/10.3390/pr11020478
https://doi.org/10.3390/pr11020478
_version_ 1772811337836003328
score 13.211869

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