A novel stochastic wastewater quality modeling based on fuzzy techniques
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RESEARCH ARTICLE
A novel stochastic wastewater quality modeling based on fuzzy techniques Khadije Lotfi 1 & Hossein Bonakdari 2 Bahram Gharabaghi 6
&
Isa Ebtehaj 1 & Robert Delatolla 3 & Ali Akbar Zinatizadeh 1,4,5 &
Received: 30 January 2020 / Accepted: 3 September 2020 # Springer Nature Switzerland AG 2020
Abstract Measurement and prediction of wastewater quality parameters are crucial for evaluating the risk to the receiving waters. This study presents new methods for the identification of outlier data and smoothing as an effective pre-processing technique prito to modelling. This new data processing method uses a combination of the autoregressive integrated moving average (ARIMA) model and -the adaptive neuro fuzzy inference system with fuzzy C-means clustering (FCM) (ANFIS-FCM). These new preprocessing methodsare compared to previously employed non-linear approaches for modelling of wastewater influent/effluent 5day biochemical oxygen demand (BOD5), chemical oxygen demand (COD) and total suspended solids (TSS). Linear modelling of each parameter, 242 linear models, were investigated, and a linear model for each parameter was selected. The results of the non-linear models led to an acceptable prediction for qualitative parameters so that the high coefficient of determination (R2) was observed for the influent and effluent BOD and TSS, respectively. The range of the R2 for all models was recorded as 0.8–0.87 and 0.83–0.89, respectively. By a combination of the linear and non-linear mothods a hybrid model was introduced. The proposed hybrid model for the influent BOD with the highest correlation between the observed and predicted values, and limited scattering was identified as the optimal model (R2 = 0.95). The use of hybrid models to predict wastewater quality parameters improved the performance and efficiency of the models. In addition, a comparison of the hybrid model with the recently developed models in the literature indicates that the developed ARIMA-ANFIS-FCM outperformed other models. Keywords Water resources . ANFIS-FCM . Fuzzy technique . Outlier detection . Stochastic model . Wastewater quality parameters
Introduction The current challenges of globally decreasing freshwater supplies and growing populations has led to an increased interest * Hossein Bonakdari [email protected] 1
Environmental Research Center, Razi University, Kermanshah, Iran
2
Department of Soils and Agri-Food Engineering, Université Laval, Québec G1V0A6, Canada
3
Department of Civil Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada
4
Applied Chemistry Department, Razi University, Kermanshah, Iran
5
Department of Environmental Sciences, University of South Africa, Pretoria, South Africa
6
School of Engineering, University of Guelph, Guelph, Ontario NIG 2W1, Canada
in wastewater treatment and the application of reused wastewaters as a water source for agriculture, urban green spaces, and industries across the world, with a particular use in arid and semi-arid regions [1–3]. The unregulated
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