A reduced-order adaptive neuro-fuzzy inference system model as a software sensor for rapid estimation of five-day biochemical oxygen demand

Autor: Seyyed Afshin Nateghi Shahrokni, Salman Safavi, Roohollah Noori
Rok vydání: 2013
Předmět:
Zdroj: Journal of Hydrology. 495:175-185
ISSN: 0022-1694
DOI: 10.1016/j.jhydrol.2013.04.052
Popis: Summary The five-day biochemical oxygen demand (BOD 5 ) is one of the key parameters in water quality management. In this study, a novel approach, i.e., reduced-order adaptive neuro-fuzzy inference system (ROANFIS) model was developed for rapid estimation of BOD 5 . In addition, an uncertainty analysis of adaptive neuro-fuzzy inference system (ANFIS) and ROANFIS models was carried out based on Monte-Carlo simulation. Accuracy analysis of ANFIS and ROANFIS models based on both developed discrepancy ratio and threshold statistics revealed that the selected ROANFIS model was superior. Pearson correlation coefficient (R) and root mean square error for the best fitted ROANFIS model were 0.96 and 7.12, respectively. Furthermore, uncertainty analysis of the developed models indicated that the selected ROANFIS had less uncertainty than the ANFIS model and accurately forecasted BOD 5 in the Sefidrood River Basin. Besides, the uncertainty analysis also showed that bracketed predictions by 95% confidence bound and d -factor in the testing steps for the selected ROANFIS model were 94% and 0.83, respectively.
Databáze: OpenAIRE