An artificial neural networks approach and hybrid method with wavelet transform to investigate the quality of Tallo River, Indonesia
Water contamination has always been one of the greatest intense environmental issues. Rivers are more polluted than the other surface and underground water resources, since passing through different areas. The current study aimed to examine the exactitude of artificial neural networks (ANN) and wave...
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| Format: | Article |
| Language: | English |
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University of Guilan
2023-07-01
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| Series: | Caspian Journal of Environmental Sciences |
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| Online Access: | https://cjes.guilan.ac.ir/article_6942_000631d6e5351793db77ca9cc52f10e7.pdf |
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| author | Dahlan Abdullah Kristina Gartsiyanova Khurramova (Eshmamatova) Madina Mansur qizi Eshkobilov Akhmad Javlievich Mullabayev Baxtiyarjon Bulturbayevich Gavxar Zokirova Mohd Norazmi Nordin |
| author_facet | Dahlan Abdullah Kristina Gartsiyanova Khurramova (Eshmamatova) Madina Mansur qizi Eshkobilov Akhmad Javlievich Mullabayev Baxtiyarjon Bulturbayevich Gavxar Zokirova Mohd Norazmi Nordin |
| author_sort | Dahlan Abdullah |
| collection | DOAJ |
| description | Water contamination has always been one of the greatest intense environmental issues. Rivers are more polluted than the other surface and underground water resources, since passing through different areas. The current study aimed to examine the exactitude of artificial neural networks (ANN) and wavelet-ANN (WANN) models in estimating the concentrations of pollutants including Cl, EC, Mg, and TDS by comparing the results of the observed data. Tallo River in Indonesia was selected as the case study. The concentrations of pollutant parameters Cl, EC, Mg, and TDS were available and used between 2010 and 2022. Then 70% (100 months) of the data were considered as training data, while 30% (44 months) were supposed to be the testing ones. ANN and WANN models were examined to evaluate and predict the concentrations of pollutants in river water. The results of each model were compared to the observed data, and the models' accuracy was assessed. The results demonstrated that applying wavelet transform improved the precision of simulation. All efficiency criteria associated with the WANN model yielded superior results compared to the ANN model. The findings indicated that using the hybrid method with wavelet transformation ameliorated the ANN model's exactitude by 10% during training and 16% during testing. Finally, the findings exhibited that the WANN method is better than ANN; consequently, the former has performed more exactitude modeling in the estimation of water quality. |
| format | Article |
| id | doaj-art-8f6ae8d2a475474b877be5e8ac64531a |
| institution | DOAJ |
| issn | 1735-3033 1735-3866 |
| language | English |
| publishDate | 2023-07-01 |
| publisher | University of Guilan |
| record_format | Article |
| series | Caspian Journal of Environmental Sciences |
| spelling | doaj-art-8f6ae8d2a475474b877be5e8ac64531a2025-08-20T02:52:31ZengUniversity of GuilanCaspian Journal of Environmental Sciences1735-30331735-38662023-07-0121364765610.22124/cjes.2023.69426942An artificial neural networks approach and hybrid method with wavelet transform to investigate the quality of Tallo River, IndonesiaDahlan Abdullah0Kristina Gartsiyanova1Khurramova (Eshmamatova) Madina Mansur qizi2Eshkobilov Akhmad Javlievich3Mullabayev Baxtiyarjon Bulturbayevich4Gavxar Zokirova5Mohd Norazmi Nordin6Department of Information Technology, University of Malikussaleh, Aceh, IndonesiaNational Institute of Geophysics, Geodesy and Geography, Hydrology and Water Management Research Center, Bulgarian Academy of Sciences (NIGGG-BAS), Sofia, Bulgaria, Acad. G. Bonchev Str., bl. 3, Sofia 1113, BulgariaFaculty of Finance and Accounting, Department of "Financial Analysis and Audit" Tashkent State University of Economics, Tashkent, Uzbekistan, Islom Karimov 49, Tashkent 100066Assistant of Termez Institute of Agrotechnologies and Innovative Development, Termez, Uzbekistan. Yangiabad mahalla, Termez district, Surkhandarya region, 191200, UzbekistanDepartment of Economics, Namangan Engineering-Construction Institute, Namangan, Republic of UzbekistanDepartment of Finance, Termez State University, UzbekistanCentre of Research for Education and Community Wellbeing, Faculty of Education, Universiti Kebangsaan Malaysia, Bangi, Selangor, MalaysiaWater contamination has always been one of the greatest intense environmental issues. Rivers are more polluted than the other surface and underground water resources, since passing through different areas. The current study aimed to examine the exactitude of artificial neural networks (ANN) and wavelet-ANN (WANN) models in estimating the concentrations of pollutants including Cl, EC, Mg, and TDS by comparing the results of the observed data. Tallo River in Indonesia was selected as the case study. The concentrations of pollutant parameters Cl, EC, Mg, and TDS were available and used between 2010 and 2022. Then 70% (100 months) of the data were considered as training data, while 30% (44 months) were supposed to be the testing ones. ANN and WANN models were examined to evaluate and predict the concentrations of pollutants in river water. The results of each model were compared to the observed data, and the models' accuracy was assessed. The results demonstrated that applying wavelet transform improved the precision of simulation. All efficiency criteria associated with the WANN model yielded superior results compared to the ANN model. The findings indicated that using the hybrid method with wavelet transformation ameliorated the ANN model's exactitude by 10% during training and 16% during testing. Finally, the findings exhibited that the WANN method is better than ANN; consequently, the former has performed more exactitude modeling in the estimation of water quality.https://cjes.guilan.ac.ir/article_6942_000631d6e5351793db77ca9cc52f10e7.pdfwater pollutiontallo riverartificial neural networkswavelet transform |
| spellingShingle | Dahlan Abdullah Kristina Gartsiyanova Khurramova (Eshmamatova) Madina Mansur qizi Eshkobilov Akhmad Javlievich Mullabayev Baxtiyarjon Bulturbayevich Gavxar Zokirova Mohd Norazmi Nordin An artificial neural networks approach and hybrid method with wavelet transform to investigate the quality of Tallo River, Indonesia Caspian Journal of Environmental Sciences water pollution tallo river artificial neural networks wavelet transform |
| title | An artificial neural networks approach and hybrid method with wavelet transform to investigate the quality of Tallo River, Indonesia |
| title_full | An artificial neural networks approach and hybrid method with wavelet transform to investigate the quality of Tallo River, Indonesia |
| title_fullStr | An artificial neural networks approach and hybrid method with wavelet transform to investigate the quality of Tallo River, Indonesia |
| title_full_unstemmed | An artificial neural networks approach and hybrid method with wavelet transform to investigate the quality of Tallo River, Indonesia |
| title_short | An artificial neural networks approach and hybrid method with wavelet transform to investigate the quality of Tallo River, Indonesia |
| title_sort | artificial neural networks approach and hybrid method with wavelet transform to investigate the quality of tallo river indonesia |
| topic | water pollution tallo river artificial neural networks wavelet transform |
| url | https://cjes.guilan.ac.ir/article_6942_000631d6e5351793db77ca9cc52f10e7.pdf |
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