Improving SVR and ANFIS performance using wavelet transform and PCA algorithm for modeling and predicting biochemical oxygen demand (BOD)

Abstract In recent years, the use of the artificial intelligence as an acceptable method in various issues, particularly in hydrology, have sharply risen. In this study, Support Vector Regression (SVR) and Adaptive Neural Fuzzy Inference System (ANFIS) models were used for predicting Biochemical Oxygen Demand (BOD) in Karun River in the west of Iran. In order to analyze hybrid models, wavelet transform was used as well. After decomposing parameters by wavelet transform, Principal Component Analysis (PCA) was used to recognize important components. Then, monthly time series of BOD index was used in Karun River in Mollasani station and also, covariates like Dissolved Oxygen (DO), monthly temperature, and river flow were used from 2002 to 2014. The results indicated that the SVR model with RMSE = 0.0338 mg/l and R2 = 0.843 has better performance than the ANFIS model with R2 = 0.828. Also, applying the wavelet transform on input data of the SVR model improved the results to R2 = 0.937 and RMSE = 0.0210 mg/l. Therefore, combining the SVR with the wavelet transform (WSVR) was a good idea to improve the prediction of the BOD value in Karun River. Finally, the combination was recognized as a suitable method and the BOD was predicted in six months.

[1]  J. Berlamont,et al.  Modelling of dissolved oxygen and biochemical oxygen demand in river water using a detailed and a simplified model , 2003 .

[2]  Vassilios A. Tsihrintzis,et al.  Fuzzy logic models for BOD removal prediction in free-water surface constructed wetlands , 2013 .

[3]  Dinesh Mohan,et al.  Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India)--a case study. , 2004, Water research.

[4]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[5]  Archana Sarkar,et al.  River Water Quality Modelling Using Artificial Neural Network Technique , 2015 .

[6]  Wu Hong.  THE APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN THE RESOURCES AND ENVIRONMENT , 2000 .

[7]  Paresh Chandra Deka,et al.  Support vector machine applications in the field of hydrology: A review , 2014, Appl. Soft Comput..

[8]  Kulwinder Singh Parmar,et al.  Wavelet and statistical analysis of river water quality parameters , 2013, Appl. Math. Comput..

[9]  Vahid Nourani,et al.  A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling , 2009 .

[10]  Xuerui Zhang,et al.  A novel hybrid water quality time series prediction method based on cloud model and fuzzy forecasting , 2015 .

[11]  Ahmed El-Shafie,et al.  Prediction of johor river water quality parameters using artificial neural networks , 2009 .

[12]  Marc Thuillard.  Wavelets in Soft Computing , 2001, World Scientific Series in Robotics and Intelligent Systems.

[13]  Souad Riad,et al.  Rainfall-runoff model usingan artificial neural network approach , 2004, Math. Comput. Model..

[14]  Mohamad Javad Alizadeh,et al.  Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean. , 2015, Marine pollution bulletin.

[15]  R. Cattell.  The Scree Test For The Number Of Factors. , 1966, Multivariate behavioral research.

[16]  Deborah V. Chapman,et al.  Water Quality Assessments , 1992 .

[17]  Ali Danandeh Mehr,et al.  A comparative analysis among computational intelligence techniques for dissolved oxygen prediction in Delaware River , 2017 .

[18]  Ahmed El-Shafie,et al.  Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring , 2014, Environmental Science and Pollution Research.

[19]  Ozgur Kisi,et al.  Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data , 2009 .

[20]  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.

[21]  Syed Mustakim Ali Shah,et al.  Application of adaptive neuro-fuzzy inference system (ANFIS) to estimate the biochemical oxygen demand (BOD) of Surma River , 2017 .

[22]  K. P. Sudheer,et al.  A neuro-fuzzy computing technique for modeling hydrological time series , 2004 .

[23]  Chuen-Tsai Sun,et al.  Neuro-fuzzy And Soft Computing: A Computational Approach To Learning And Machine Intelligence [Books in Brief] , 1997, IEEE Transactions on Neural Networks.

[24]  G. Hutcheson.  The Multivariate Social Scientist: Introductory Statistics Using Generalized Linear Models , 1999 .

[25]  A K Chapagain,et al.  An improved water footprint methodology linking global consumption to local water resources: a case of Spanish tomatoes. , 2009, Journal of environmental management.

[26]  Ozgur Kisi,et al.  Evolutionary fuzzy models for river suspended sediment concentration estimation. , 2009 .

[27]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[28]  Soichi Nishiyama,et al.  A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff , 2007 .

[29]  J. Wayland Eheart,et al.  Evaluation of Neural Networks for Modeling Nitrate Concentrations in Rivers , 2003 .

[30]  T. Ross.  Fuzzy Logic with Engineering Applications , 1994 .

[31]  Ehsan Olyaie,et al.  PERFORMANCE EVALUATION OF ARTIFICIAL NEURAL NETWORKS FOR PREDICTING RIVERS WATER QUALITY INDICES (BOD AND DO) IN HAMADAN MORAD BEIK RIVER , 2010 .

[32]  Christos S. Akratos,et al.  An artificial neural network model and design equations for BOD and COD removal prediction in horizontal subsurface flow constructed wetlands , 2008 .

[33]  H. Loáiciga,et al.  Assimilative Capacity and Flow Dilution for Water Quality Protection in Rivers , 2015 .

[34]  H R Safavi.  PREDICTION OF RIVER WATER QUALITY BY ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS) , 2010 .

[35]  Gholamreza Asadollahfardi,et al.  Application of Artificial Neural Network to Predict TDS in Talkheh Rud River , 2012 .

[36]  Chuntian Cheng,et al.  Long-Term Prediction of Discharges in Manwan Hydropower Using Adaptive-Network-Based Fuzzy Inference Systems Models , 2005, ICNC.

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

[39]  Chuanqi Zhang,et al.  Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China , 2013, Environmental Monitoring and Assessment.