River stage prediction based on a distributed support vector regression

Abstract

An accurate and timely prediction of river flow flooding can provide time for the authorities to take pertinent flood protection measures such as evacuation. Various data-derived models including LR (linear regression), NNM (the nearest-neighbor method) ANN (artificial neural network) and SVR (support vector regression), have been successfully applied to water level prediction. Of them, SVR is particularly highly valued, because it has the advantage over many data-derived models in overcoming overfitting of training data. However, SVR is computationally time-consuming when used to solve large-size problems. In the context of river flow prediction, equipped with LR model as a benchmark and genetic algorithm-based ANN (ANN-GA) and NNM as counterparts, a novel distributed SVR (D-SVR) model is proposed in this study. It implements a local approximation to training data because partitioned original training data are independently fitted by each local SVR model. ANN-GA and LR models are also used to help determine input variables. A two-step GA algorithm is employed to find the optimal triplets (C, ε, σ) for D-SVR model. The validation results reveal that the proposed D-SVR model can carry out the river flow prediction better in comparison with others, and dramatically reduce the training time compared with the conventional SVR model. The pivotal factor contributing to the performance of D-SVR may be that it implements a local approximation method and the principle of structural risk minimization.