K-nearest neighbor based computational intelligence and RSM predictive models for extraction of Cadmium from contaminated soil
Computational intelligence (CI) predictive models based on k-Nearest Neighbor (KNN) algorithms were developed for Cd ions removal from contaminated soil using environmentally friendly chelating-agent polyaspartate. Based on extracted Cd ions into the chelating-agent, residual Cd ions in treated soil...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-04-01
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| Series: | Ain Shams Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447922002556 |
| _version_ | 1797839077687427072 |
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| author | Nuhu Dalhat Mu'azu Sunday Olusanya Olatunji |
| author_facet | Nuhu Dalhat Mu'azu Sunday Olusanya Olatunji |
| author_sort | Nuhu Dalhat Mu'azu |
| collection | DOAJ |
| description | Computational intelligence (CI) predictive models based on k-Nearest Neighbor (KNN) algorithms were developed for Cd ions removal from contaminated soil using environmentally friendly chelating-agent polyaspartate. Based on extracted Cd ions into the chelating-agent, residual Cd ions in treated soil and Cd removal efficiency, the performances of the KNN models were compared with response surface methodology (RSM) models using whole data set (KNN1) and split data (KNN2) scenarios using correlation coefficient (R2) and root mean square error (RMSE). Optimal performances of the developed KNN based models were found to be significantly influenced by the nearest neighbor’s k-parameter attributed to the disparity in the two approaches. The KNN1 demonstrated better performances characterized by higher R2 = 0.984–0.999 and lower RSME of 0.399–6 against the RSM models’ R2 = 0.7882–0.990 and RSME 2.08–20.36, respectively. For the KNN2 models, even though lower performances were obtained, yet the soil remediation efficiency models, demonstrated enhanced performance over the RSM models. |
| first_indexed | 2024-04-09T15:52:24Z |
| format | Article |
| id | doaj.art-d8f83b1a907f4a8cbb9db937efcaeac4 |
| institution | Directory Open Access Journal |
| issn | 2090-4479 |
| language | English |
| last_indexed | 2024-04-09T15:52:24Z |
| publishDate | 2023-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj.art-d8f83b1a907f4a8cbb9db937efcaeac42023-04-26T05:57:57ZengElsevierAin Shams Engineering Journal2090-44792023-04-01144101944K-nearest neighbor based computational intelligence and RSM predictive models for extraction of Cadmium from contaminated soilNuhu Dalhat Mu'azu0Sunday Olusanya Olatunji1Department of Environmental Engineering, College of Engineering, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, 31451 Dammam, Saudi Arabia; Corresponding author.College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi ArabiaComputational intelligence (CI) predictive models based on k-Nearest Neighbor (KNN) algorithms were developed for Cd ions removal from contaminated soil using environmentally friendly chelating-agent polyaspartate. Based on extracted Cd ions into the chelating-agent, residual Cd ions in treated soil and Cd removal efficiency, the performances of the KNN models were compared with response surface methodology (RSM) models using whole data set (KNN1) and split data (KNN2) scenarios using correlation coefficient (R2) and root mean square error (RMSE). Optimal performances of the developed KNN based models were found to be significantly influenced by the nearest neighbor’s k-parameter attributed to the disparity in the two approaches. The KNN1 demonstrated better performances characterized by higher R2 = 0.984–0.999 and lower RSME of 0.399–6 against the RSM models’ R2 = 0.7882–0.990 and RSME 2.08–20.36, respectively. For the KNN2 models, even though lower performances were obtained, yet the soil remediation efficiency models, demonstrated enhanced performance over the RSM models.http://www.sciencedirect.com/science/article/pii/S2090447922002556Soil washingHeavy metals soil remediationFriendly chelating agentsArtificial intelligence modelingResponse surface modelingBiodegradable polymers |
| spellingShingle | Nuhu Dalhat Mu'azu Sunday Olusanya Olatunji K-nearest neighbor based computational intelligence and RSM predictive models for extraction of Cadmium from contaminated soil Ain Shams Engineering Journal Soil washing Heavy metals soil remediation Friendly chelating agents Artificial intelligence modeling Response surface modeling Biodegradable polymers |
| title | K-nearest neighbor based computational intelligence and RSM predictive models for extraction of Cadmium from contaminated soil |
| title_full | K-nearest neighbor based computational intelligence and RSM predictive models for extraction of Cadmium from contaminated soil |
| title_fullStr | K-nearest neighbor based computational intelligence and RSM predictive models for extraction of Cadmium from contaminated soil |
| title_full_unstemmed | K-nearest neighbor based computational intelligence and RSM predictive models for extraction of Cadmium from contaminated soil |
| title_short | K-nearest neighbor based computational intelligence and RSM predictive models for extraction of Cadmium from contaminated soil |
| title_sort | k nearest neighbor based computational intelligence and rsm predictive models for extraction of cadmium from contaminated soil |
| topic | Soil washing Heavy metals soil remediation Friendly chelating agents Artificial intelligence modeling Response surface modeling Biodegradable polymers |
| url | http://www.sciencedirect.com/science/article/pii/S2090447922002556 |
| work_keys_str_mv | AT nuhudalhatmuazu knearestneighborbasedcomputationalintelligenceandrsmpredictivemodelsforextractionofcadmiumfromcontaminatedsoil AT sundayolusanyaolatunji knearestneighborbasedcomputationalintelligenceandrsmpredictivemodelsforextractionofcadmiumfromcontaminatedsoil |