Prediction of biochemical oxygen demand at the upstream catchment of a reservoir using adaptive neuro fuzzy inference system
The aim of this study is to examine the potential of adaptive neuro fuzzy inference system (ANFIS) to estimate biochemical oxygen demand (BOD). To illustrate the applicability of ANFIS method, the upstream catchment of Feitsui Reservoir in Taiwan is chosen as the case study area. The appropriate inp...
| Veröffentlicht in: | Water science and technology : a journal of the International Association on Water Pollution Research. - 1986. - 76(2017), 7-8 vom: 01. Okt., Seite 1739-1753 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2017
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| Zugriff auf das übergeordnete Werk: | Water science and technology : a journal of the International Association on Water Pollution Research |
| Schlagworte: | Journal Article Oxygen S88TT14065 |
| Zusammenfassung: | The aim of this study is to examine the potential of adaptive neuro fuzzy inference system (ANFIS) to estimate biochemical oxygen demand (BOD). To illustrate the applicability of ANFIS method, the upstream catchment of Feitsui Reservoir in Taiwan is chosen as the case study area. The appropriate input variables used to develop the ANFIS models are determined based on the t-test. The results obtained by ANFIS are compared with those by multiple linear regression (MLR) and artificial neural networks (ANNs). Simulated results show that the identified ANFIS model is superior to the traditional MLR and nonlinear ANNs models in terms of the performance evaluated by the Pearson coefficient of correlation, the root mean square error, the mean absolute percentage, and the mean absolute error. These results indicate that ANFIS models are more suitable than ANNs or MLR models to predict the nonlinear relationship within the variables caused by the complexity of aquatic systems and to produce the best fit of the measured BOD concentrations. ANFIS can be seen as a powerful predictive alternative to traditional water quality modeling techniques and extended to other areas to improve the understanding of river pollution trends |
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| Beschreibung: | Date Completed 08.01.2018 Date Revised 10.12.2019 published: Print Citation Status MEDLINE |
| ISSN: | 0273-1223 |
| DOI: | 10.2166/wst.2017.359 |