Modeling of Dissolved Oxygen Applying Stepwise Regression and a Template-Based Fuzzy Logic System

This paper develops a template-based fuzzy inference system (TFIS) capable of simulating the dissolved oxygen (DO) concentration of an example stream by using daily data (2009–2012). Stepwise regression (SR) analysis and Mallows’ Cp statistic were employed to select the best set of independent parameters for the input vector of the TFIS and the regressive models. The most effective inputs were determined for temperature and specific conductivity (P-value=0.000), whereas flow rate (P-value=0.002) and pH (P-value=0.004) were found to be less effective parameters. The TFIS and SR models undersimulated the magnitude of high DO concentrations (>12.5  mg/L), whereas the low and medium DO concentrations were oversimulated. However, they were closer to the observed data. A comparison of the prediction accuracy of the TFIS and SR methods indicated that the TFIS approach was more accurate in simulating DO concentrations, except for low concentrations of DO (<10  mg/L). The TFIS method was found to be superior to the conventional SR model.

[1]  Soichi Nishiyama,et al.  Analysis and prediction of flow from local source in a river basin using a Neuro-fuzzy modeling tool. , 2007, Journal of environmental management.

[2]  Drasko Furundzic,et al.  Application example of neural networks for time series analysis: : Rainfall-runoff modeling , 1998, Signal Process..

[3]  Jan-Tai Kuo,et al.  USING ARTIFICIAL NEURAL NETWORK FOR RESERVOIR EUTROPHICATION PREDICTION , 2007 .

[4]  Stefano Marsili-Libelli,et al.  Spatio-temporal dissolved oxygen dynamics in the Orbetello lagoon by fuzzy pattern recognition , 2009 .

[5]  J. Schnoor,et al.  High-Frequency Diel Dissolved Oxygen Stream Data Modeled for Variable Temperature and Scale , 2009 .

[6]  Long Jiao,et al.  QSPR studies on the aqueous solubility of PCDD/Fs by using artificial neural network combined with stepwise regression , 2010 .

[7]  Saeed-Reza Sabbagh-Yazdi,et al.  Estimation of current-induced scour depth around pile groups using neural network and adaptive neuro-fuzzy inference system , 2009, Appl. Soft Comput..

[8]  P. Bedient,et al.  Stream Dissolved Oxygen Analysis and Control , 1985 .

[9]  Ebrahim Mamdani,et al.  Applications of fuzzy algorithms for control of a simple dynamic plant , 1974 .

[10]  Mohammad Teshnehlab,et al.  Using adaptive neuro-fuzzy inference system for hydrological time series prediction , 2008, Appl. Soft Comput..

[11]  Mahmud Güngör,et al.  River flow estimation using adaptive neuro fuzzy inference system , 2007, Math. Comput. Simul..

[12]  Sever Arslan,et al.  Evaluation of Input Variables in Adaptive-Network-Based Fuzzy Inference System Modeling for an Anaerobic Wastewater Treatment Plant under Unsteady State , 2007 .

[13]  Durdu Ömer Faruk.  A hybrid neural network and ARIMA model for water quality time series prediction , 2010, Eng. Appl. Artif. Intell..

[14]  Mohammad Zounemat-Kermani,et al.  Hydrometeorological Parameters in Prediction of Soil Temperature by Means of Artificial Neural Network: Case Study in Wyoming , 2013 .

[15]  Ibrahim A. Hameed.  Using Gaussian membership functions for improving the reliability and robustness of students' evaluation systems , 2011, Expert Syst. Appl..

[16]  Han Yan,et al.  Adaptive neuro fuzzy inference system for classification of water quality status. , 2010, Journal of environmental sciences.

[17]  Vahid Nourani,et al.  A geomorphology-based ANFIS model for multi-station modeling of rainfall–runoff process , 2013 .

[18]  Dingjiang Chen,et al.  Artificial neural network modelling of concentrations of nitrogen, phosphorus and dissolved oxygen in a non‐point source polluted river in Zhejiang Province, southeast China , 2009 .

[19]  K. Vairavamoorthy,et al.  Contaminant Transport Model for Unsaturated Soil Using Fuzzy Approach , 2006 .

[20]  Jasna Radulović,et al.  Neural network modeling of dissolved oxygen in the Gruža reservoir, Serbia , 2010 .

[21]  Mehmet Cakmakci,et al.  Fuzzy logic modeling of the dissolved oxygen fluctuations in Golden Horn , 2005 .

[22]  Miguel A. Mariño,et al.  Rule-Based Fuzzy System for Assessing Groundwater Vulnerability , 2007 .

[23]  Sirilak Areerachakul,et al.  Comparison of ANFIS and ANN for Estimation of Biochemical Oxygen Demand Parameter in Surface Water , 2012 .

[24]  Paramasivan Saratchandran,et al.  Sequential Adaptive Fuzzy Inference System (SAFIS) for nonlinear system identification and prediction , 2006, Fuzzy Sets Syst..

[25]  A. Malik,et al.  Artificial neural network modeling of the river water quality—A case study , 2009 .

[26]  E. Doğan,et al.  Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique. , 2009, Journal of environmental management.