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

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.