A novel stochastic wastewater quality modeling based on fuzzy techniques.
作者： Khadije Lotfi , Hossein Bonakdari , Isa Ebtehaj , Robert Delatolla , Ali Akbar Zinatizadeh
DOI: 10.1007/S40201-020-00530-8
关键词:
摘要: Measurement and prediction of wastewater quality parameters are crucial for evaluating the risk to receiving waters. This study presents new methods identification outlier data smoothing as an effective pre-processing technique prito modelling. processing method uses a combination autoregressive integrated moving average (ARIMA) model -the adaptive neuro fuzzy inference system with C-means clustering (FCM) (ANFIS-FCM). These methodsare compared previously employed non-linear approaches modelling influent/effluent 5-day biochemical oxygen demand (BOD5), chemical (COD) total suspended solids (TSS). Linear each parameter, 242 linear models, were investigated, parameter was selected. The results models led acceptable qualitative so that high coefficient determination (R2) observed influent effluent BOD TSS, respectively. range R2 all recorded 0.8–0.87 0.83–0.89, By mothods hybrid introduced. proposed highest correlation between predicted values, limited scattering identified optimal (R2 = 0.95). use predict improved performance efficiency models. In addition, comparison recently developed in literature indicates ARIMA-ANFIS-FCM outperformed other
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