An Intelligent Carbon-Based Prediction of Wastewater Treatment Plants Using Machine Learning Algorithms
Purification of polluted water and return back to the agriculture field is the wastewater treatment for plants. Contaminated water causes illness and health emergencies of public. Also, health risk due release of toxic contaminants brings problem to all living beings. At present, sensors are used in...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publications
2022-01-01
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| Series: | Adsorption Science & Technology |
| Online Access: | http://dx.doi.org/10.1155/2022/8448489 |
| _version_ | 1797288067063611392 |
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| author | Anwer Mustafa Hilal Maha M. Althobaiti Taiseer Abdalla Elfadil Eisa Rana Alabdan Manar Ahmed Hamza Abdelwahed Motwakel Mesfer Al Duhayyim Noha Negm |
| author_facet | Anwer Mustafa Hilal Maha M. Althobaiti Taiseer Abdalla Elfadil Eisa Rana Alabdan Manar Ahmed Hamza Abdelwahed Motwakel Mesfer Al Duhayyim Noha Negm |
| author_sort | Anwer Mustafa Hilal |
| collection | DOAJ |
| description | Purification of polluted water and return back to the agriculture field is the wastewater treatment for plants. Contaminated water causes illness and health emergencies of public. Also, health risk due release of toxic contaminants brings problem to all living beings. At present, sensors are used in waste water treatment and transfer data via internet of things (IoT). Prediction of wastewater quality content which is presence of total nitrogen (T-N) and total phosphorous (T-P) elements, chemical oxygen demand (COD), biochemical demand (BOD), and total suspended solids (TSS) is associated with eutrophication that should be prevented. This may leads to algal bloom and spoils aquatic life which is consumed by human. The presence of nitrogen and phosphorous elements is in the content of wastewater, and these elements are associated with eutrophication which should be prevented. Adsorption of T-N and T-P activated carbon was predictable as one of the most promising methods for wastewater treatment. Many research works have been done. The issues are inefficiency in the prediction of wastewater treatment. To overcome this issue, this paper proposed fusion of B-KNN with the ELM algorithm that is used. The accuracy of the BKNN-ELM algorithm in classification of water quality status produced the highest accuracy of the highest accuracy which is K=9 and k=10 with rate of accuracy which is 93.56%, and the lowest accuracy is K=1 of 65.34%. Experiment evaluation shows that a total suspended solid predicted by proposed model is 91 with accuracy of 93%. The relative error rate of prediction is 12.03 which is lesser than existing models. |
| first_indexed | 2024-03-07T18:43:57Z |
| format | Article |
| id | doaj.art-e75cad3b7b6c4552bb6ddb2b6499ed86 |
| institution | Directory Open Access Journal |
| issn | 2048-4038 |
| language | English |
| last_indexed | 2024-03-07T18:43:57Z |
| publishDate | 2022-01-01 |
| publisher | SAGE Publications |
| record_format | Article |
| series | Adsorption Science & Technology |
| spelling | doaj.art-e75cad3b7b6c4552bb6ddb2b6499ed862024-03-02T03:15:51ZengSAGE PublicationsAdsorption Science & Technology2048-40382022-01-01202210.1155/2022/8448489An Intelligent Carbon-Based Prediction of Wastewater Treatment Plants Using Machine Learning AlgorithmsAnwer Mustafa Hilal0Maha M. Althobaiti1Taiseer Abdalla Elfadil Eisa2Rana Alabdan3Manar Ahmed Hamza4Abdelwahed Motwakel5Mesfer Al Duhayyim6Noha Negm7Department of Computer and Self DevelopmentDepartment of Computer ScienceDepartment of Information Systems-Girls SectionDepartment of Information SystemsDepartment of Computer and Self DevelopmentDepartment of Computer and Self DevelopmentDepartment of Natural and Applied SciencesDepartment of Information Systems-Girls SectionPurification of polluted water and return back to the agriculture field is the wastewater treatment for plants. Contaminated water causes illness and health emergencies of public. Also, health risk due release of toxic contaminants brings problem to all living beings. At present, sensors are used in waste water treatment and transfer data via internet of things (IoT). Prediction of wastewater quality content which is presence of total nitrogen (T-N) and total phosphorous (T-P) elements, chemical oxygen demand (COD), biochemical demand (BOD), and total suspended solids (TSS) is associated with eutrophication that should be prevented. This may leads to algal bloom and spoils aquatic life which is consumed by human. The presence of nitrogen and phosphorous elements is in the content of wastewater, and these elements are associated with eutrophication which should be prevented. Adsorption of T-N and T-P activated carbon was predictable as one of the most promising methods for wastewater treatment. Many research works have been done. The issues are inefficiency in the prediction of wastewater treatment. To overcome this issue, this paper proposed fusion of B-KNN with the ELM algorithm that is used. The accuracy of the BKNN-ELM algorithm in classification of water quality status produced the highest accuracy of the highest accuracy which is K=9 and k=10 with rate of accuracy which is 93.56%, and the lowest accuracy is K=1 of 65.34%. Experiment evaluation shows that a total suspended solid predicted by proposed model is 91 with accuracy of 93%. The relative error rate of prediction is 12.03 which is lesser than existing models.http://dx.doi.org/10.1155/2022/8448489 |
| spellingShingle | Anwer Mustafa Hilal Maha M. Althobaiti Taiseer Abdalla Elfadil Eisa Rana Alabdan Manar Ahmed Hamza Abdelwahed Motwakel Mesfer Al Duhayyim Noha Negm An Intelligent Carbon-Based Prediction of Wastewater Treatment Plants Using Machine Learning Algorithms Adsorption Science & Technology |
| title | An Intelligent Carbon-Based Prediction of Wastewater Treatment Plants Using Machine Learning Algorithms |
| title_full | An Intelligent Carbon-Based Prediction of Wastewater Treatment Plants Using Machine Learning Algorithms |
| title_fullStr | An Intelligent Carbon-Based Prediction of Wastewater Treatment Plants Using Machine Learning Algorithms |
| title_full_unstemmed | An Intelligent Carbon-Based Prediction of Wastewater Treatment Plants Using Machine Learning Algorithms |
| title_short | An Intelligent Carbon-Based Prediction of Wastewater Treatment Plants Using Machine Learning Algorithms |
| title_sort | intelligent carbon based prediction of wastewater treatment plants using machine learning algorithms |
| url | http://dx.doi.org/10.1155/2022/8448489 |
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