Predicting Water Quality Indicators from Conventional and Nonconventional Water Resources in Algeria Country: Adaptive N
Monitoring water quality is of great importance and mainly adopted for water pollution control of conventional and nonconventional water resources. Generally, water quality is evaluated using several indicators, including chemical oxygen demand (COD), bio
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Contents 1 Introduction 2 Wastewater and Drinking Water Datasets 3 Methodology 3.1 Multilayer Perceptron Neural Network (MLPNN) 3.2 Adaptive Neuro-Fuzzy Inference System (ANFIS) 3.3 Multiple Linear Regression (MLR) 3.4 Performance Assessment of the Models 4 Results S. Heddam (*) Faculty of Science, Agronomy Department, Hydraulics Division, Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology, University 20 Août 1955, Skikda, Algeria e-mail: [email protected] O. Kisi School of Technology, Ilia State University, Tbilisi, Georgia e-mail: [email protected] A. Sebbar Soil and Hydraulics Laboratory, Faculty of Engineering Sciences, Hydraulics Department, University Badji-Mokhtar Annaba, Annaba, Algeria e-mail: [email protected] L. Houichi Department of Hydraulic, University of Batna 2, Batna, Algeria e-mail: [email protected] L. Djemili Research Laboratory of Natural Resources and Adjusting, Faculty of Engineering Sciences, Hydraulics Department, University Badji-Mokhtar Annaba, Annaba, Algeria e-mail: [email protected] Abdelazim Negm, Bouderbala Abdelkader, Haroun Chenchouni, and Damia Barcelo (eds.), Water Resources in Algeria: Part II: Water Quality, Treatment, Protection and Development, Hdb Env Chem, DOI 10.1007/698_2019_399, © Springer Nature Switzerland AG 2019
S. Heddam et al. 4.1 Modeling DO at Boudouaou Drinking Water Treatment Plant 4.2 Modeling COD at Sidi Marouane Wastewater Treatment Plant 5 Discussion 6 Conclusions 7 Recommendations References
Abstract Monitoring water quality is of great importance and mainly adopted for water pollution control of conventional and nonconventional water resources. Generally, water quality is evaluated using several indicators, including chemical oxygen demand (COD), biochemical oxygen demand (BOD), and dissolved oxygen concentration (DO). In the present investigation, two artificial intelligence techniques, namely, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANN), were applied for predicting two water quality indicators: (1) chemical oxygen demand (COD) at Sidi Marouane Wastewater Treatment Plant (WWTP), east of Algeria, and (2) dissolved oxygen concentration (DO) at the drinking water treatment plant of Boudouaou, Algeria. The models were developed and compared based on several water quality variables as inputs. Three ANFIS models, namely, (1) ANFIS with fuzzy c-mean clustering (FCM) algorithm called ANFIS_FC, (2) ANFIS with grid partition (GP) method called ANFIS_GP, and (3) ANFIS with subtractive clustering (SC) called ANFIS_SC, were developed. The ANFIS models were compared to standard multilayer perceptron neural network (MLPNN) and multiple linear regression model (MLR). Results obtained demonstrated that (1) for predicting COD, ANFIS_SC is the best model, and the coefficient of correlation (R), Wilmot’s index (d), root-mean-square error (RMSE), and mean absolute error (MAE) were calculated as 0.805, 0.880, 6.742, and 4.944 mg/L for the validation dataset. The worst results were obtained u
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