Predicting biochemical oxygen demand in wastewater treatment plant using advance extreme learning machine optimized by Bat algorithm
Wastewater quality modelling plays a vital role in planning and management of wastewater treatment plants (WWTP). This paper develops a new hybrid machine learning model based on extreme learning machine (ELM) optimized by Bat algorithm (ELM-Bat) for modelling five day effluent biochemical oxygen de...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-11-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023085596 |
| _version_ | 1797429913463029760 |
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| author | Hayat Mekaoussi Salim Heddam Nouri Bouslimanni Sungwon Kim Mohammad Zounemat-Kermani |
| author_facet | Hayat Mekaoussi Salim Heddam Nouri Bouslimanni Sungwon Kim Mohammad Zounemat-Kermani |
| author_sort | Hayat Mekaoussi |
| collection | DOAJ |
| description | Wastewater quality modelling plays a vital role in planning and management of wastewater treatment plants (WWTP). This paper develops a new hybrid machine learning model based on extreme learning machine (ELM) optimized by Bat algorithm (ELM-Bat) for modelling five day effluent biochemical oxygen demand (BOD5). Specifically, this hybrid model combines the Bat algorithm for model parameters optimization and the standalone ELM. The proposed model was developed using historical measured effluents wastewater quality variables, i.e., the chemical oxygen demand (COD), temperature, pH, total suspended solid (TSS), specific conductance (SC) and the wastewater flow (Q). The performances of the hybrid ELM-Bat were compared with those of the multilayer perceptron neural network (MLPNN), the random forest regression (RFR), the Gaussian process regression (GPR), the random vector functional link network (RVFL), and the multiple linear regression (MLR) models. By comparing several input variables combination, the improvement achieved in the accuracy of prediction through the hybrid ELM-Bat was quantified. All models were first calibrated using training dataset and later tested using validation and based on four performances metrics namely, root mean square error (RMSE), mean absolute error (MAE), the correlation coefficient (R), and the Nash-Sutcliffe model efficiency (NSE). In all, it is concluded that the ELM-Bat is the most accurate model when all the six input were included as input variables, and it outperforms all other benchmark models in terms of predictive accuracy, exhibiting RMSE, MAE, R and NSE values of approximately, 0.885, 0.781, 2.621, and 1.989, respectively. |
| first_indexed | 2024-03-09T09:19:45Z |
| format | Article |
| id | doaj.art-760875e5b5054297878c20c906c07f8c |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-09T09:19:45Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-760875e5b5054297878c20c906c07f8c2023-12-02T07:01:57ZengElsevierHeliyon2405-84402023-11-01911e21351Predicting biochemical oxygen demand in wastewater treatment plant using advance extreme learning machine optimized by Bat algorithmHayat Mekaoussi0Salim Heddam1Nouri Bouslimanni2Sungwon Kim3Mohammad Zounemat-Kermani4Institute of veterinary and agronomic sciences, Agronomy Department, Hydraulics Division, University Batna 1-Hadj Lakhdar- Allées 19 mai, Route de Biskra Batna, 05000 Algeria; Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology (LRIBEB) University 20 Août 1955 Skikda, AlgeriaLaboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology (LRIBEB), Faculty of Science, Agronomy Department, University 20 Août 1955-Skikda, Route El Hadaik, BP 26, Skikda, Algeria; Corresponding authorInstitute of veterinary and agronomic sciences, Agronomy Department, Chemical Division, University Batna 1-Hadj Lakhdar- Allées 19 mai, Route de Biskra Batna, 05000 AlgeriaDepartment of Railroad Construction and Safety Engineering, Dongyang University, Yeongju 36040, Republic of KoreaDepartment of Water Engineering, Shahid Bahonar University of Kerman, Kerman, IranWastewater quality modelling plays a vital role in planning and management of wastewater treatment plants (WWTP). This paper develops a new hybrid machine learning model based on extreme learning machine (ELM) optimized by Bat algorithm (ELM-Bat) for modelling five day effluent biochemical oxygen demand (BOD5). Specifically, this hybrid model combines the Bat algorithm for model parameters optimization and the standalone ELM. The proposed model was developed using historical measured effluents wastewater quality variables, i.e., the chemical oxygen demand (COD), temperature, pH, total suspended solid (TSS), specific conductance (SC) and the wastewater flow (Q). The performances of the hybrid ELM-Bat were compared with those of the multilayer perceptron neural network (MLPNN), the random forest regression (RFR), the Gaussian process regression (GPR), the random vector functional link network (RVFL), and the multiple linear regression (MLR) models. By comparing several input variables combination, the improvement achieved in the accuracy of prediction through the hybrid ELM-Bat was quantified. All models were first calibrated using training dataset and later tested using validation and based on four performances metrics namely, root mean square error (RMSE), mean absolute error (MAE), the correlation coefficient (R), and the Nash-Sutcliffe model efficiency (NSE). In all, it is concluded that the ELM-Bat is the most accurate model when all the six input were included as input variables, and it outperforms all other benchmark models in terms of predictive accuracy, exhibiting RMSE, MAE, R and NSE values of approximately, 0.885, 0.781, 2.621, and 1.989, respectively.http://www.sciencedirect.com/science/article/pii/S2405844023085596ModellingWWTPBOD5ELMBat algorithmRVFL |
| spellingShingle | Hayat Mekaoussi Salim Heddam Nouri Bouslimanni Sungwon Kim Mohammad Zounemat-Kermani Predicting biochemical oxygen demand in wastewater treatment plant using advance extreme learning machine optimized by Bat algorithm Heliyon Modelling WWTP BOD5 ELM Bat algorithm RVFL |
| title | Predicting biochemical oxygen demand in wastewater treatment plant using advance extreme learning machine optimized by Bat algorithm |
| title_full | Predicting biochemical oxygen demand in wastewater treatment plant using advance extreme learning machine optimized by Bat algorithm |
| title_fullStr | Predicting biochemical oxygen demand in wastewater treatment plant using advance extreme learning machine optimized by Bat algorithm |
| title_full_unstemmed | Predicting biochemical oxygen demand in wastewater treatment plant using advance extreme learning machine optimized by Bat algorithm |
| title_short | Predicting biochemical oxygen demand in wastewater treatment plant using advance extreme learning machine optimized by Bat algorithm |
| title_sort | predicting biochemical oxygen demand in wastewater treatment plant using advance extreme learning machine optimized by bat algorithm |
| topic | Modelling WWTP BOD5 ELM Bat algorithm RVFL |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023085596 |
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