Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction

Abstract

Summary In the research, the role of three input selection techniques is evaluated on support vector machine (SVM) performance for prediction of monthly stream flow. First, a SVM model is adapted to predict the next monthly flow as a function of 18 input variables including monthly rainfall (R), discharge (Q), sun radiation (Rad), and temperature {as minimum (Tmin), maximum (Tmax) and average (Tave)} with three temporal delays belong to t, t-1, and t-2. Subsequently, principal component analysis (PCA), Gamma test (GT), and forward selection (FS) techniques are used to reduce the number of input variables. Upon reducing 18 input variables to 5 (using PCA and GT) and 7 (using FS techniques), they are then fed to SVM model. In addition, a proper artificial neural network (ANN) model based on PCA technique is developed (PCA-ANN). Then, comparison among the developed SVM models (PCA-SVM and GT-SVM) and PCA-ANN model is carried out. Furthermore, the imperfections of the discrepancy ratio (DR) statistic are remedied and an appropriate DR statistic is developed. Finally, the error distribution during testing step of selected models (PCA-SVM, GT-SVM, and PCA-ANN) is computed using the developed DR statistic. Results indicated that preprocessing the input variables by means of PCA and GT techniques has improved the SVM model operation and the developed models (PCA-SVM and GT-SVM) are considerably better than original SVM model. Besides, PCA-SVM is superior to GT-SVM and PCA-ANN models. Determination coefficient (R2) for PCA-SVM model was equal to 0.92 and 0.88 in the training and testing steps, respectively.