Weighted error-output recurrent echo kernel state network for multi-step water level prediction

Abstract

With development of information techniques in navigation and shipping, machine learning algorithms are applied in enhancing navigation safety. One of critical areas, which attracts lots of attention from scientists and researchers, is water level prediction. Although randomization-based algorithms obtain good performance in water level prediction, these algorithms still have their own limitations. For example, unstable prediction problem caused by random weights selection, and the error accumulation problem caused by the conventional recurrent algorithm. In this study, we combine three proposed approach with the conventional echo state network. Firstly, the Gaussian kernel method transforms the input features into a high-dimensional features, which in some extent improves the forecasting accuracy. Secondly, kernel reservoir states are proposed. It not only abandons the random selected weights, but it also makes hidden neurons to connect closely. Lastly, a novel weighted error-output recurrent multi-step algorithm is proposed. It uses previous forecasting errors to update the current output weights in order to overcome the error accumulation problem. Based on above three approaches, a multi-step water level prediction model is proposed called Weighted Error-output Recurrent Echo Kernel State Network (WER-EKSN). The experimental results and statistical analysis represent that our proposed model has better forecasting performance than other compared models. It not only has the superior ability in water level prediction, but it also provides the evidences for the management of transportation in water-land, such as flood protection, and management of ship route.