Author(s): Dawei Han; Zhiping Yang
Linked Author(s): Dawei Han
Keywords: Hydroinformatics; Artificial intelligence; River flow; Supportvector machines
Abstract: Recently, a new tool from the Artificial Intelligence field called a Support Vector Machine (SVM) has gained popularity in the Machine Learning community. It has been applied successfully to classification tasks such as pattern recognition, OCR and more recently also to regression and time series. In recent years, a number of non-linear classification and regression SVMs have been developed and these have been benchmarked against artificial neural networks (ANNs). It has been found that the empirical performance of SVMs is generally as good as the best ANN solutions. Compared with traditional artificial neural networks, learning in SVMs is very robust from the point of view of the precision of the computations. This paper describes the attempt of using Support Vector Machines approach for river flow modelling. Mathematically, SVMs are a range of classification and regression algorithms that have been formulated from the principles of statistical learning theory. The nonlinearity and learning abilities in the SVM technique are useful features that could be applied to many areas in future hydraulic and hydrological engineering.