Application of support vector machine in lake water level prediction

This paper examines the potential of the support vector machine (SVM) in long-term prediction of lake water levels. Lake Erie mean monthly water levels from 1918 to 2001 are used to predict future water levels up to 12 months ahead. The results are compared with a widely used neural network model called a multilayer perceptron (MLP) and with a conventional multiplicative seasonal autoregressive model (SAR). Overall, the SVM showed good performance and is proved to be competitive with the MLP and SAR models. For a 3- to 12-month-ahead prediction, the SVM model outperforms the two other models based on root-mean square error and correlation coefficient performance criteria. Furthermore, the SVM exhibits inherent advantages due to its use of the structural risk minimization principle in formulating cost functions and of quadratic programming during model optimization. These advantages lead to a unique optimal and global solution compared to conventional neural network models.