Reservoir Computing approach to Great Lakes water level forecasting

Abstract

Summary The use of echo state network (ESN) for dynamical system modeling is known as Reservoir Computing and has been shown to be effective for a number of applications, including signal processing, learning grammatical structure, time series prediction and motor/system control. However, the performance of Reservoir Computing approach on hydrological time series remains largely unexplored. This study investigates the potential of ESN or Reservoir Computing for long-term prediction of lake water levels. Great Lakes water levels from 1918 to 2005 are used to develop and evaluate the ESN models. The forecast performance of the ESN-based models is compared with the results obtained from two benchmark models, the conventional recurrent neural network (RNN) and the Bayesian neural network (BNN). The test results indicate a strong ability of ESN models to provide improved lake level forecasts up to 10-month ahead – suggesting that the inherent structure and innovative learning approach of the ESN is suitable for hydrological time series modeling. Another particular advantage of ESN learning approach is that it simplifies the network training complexity and avoids the limitations inherent to the gradient descent optimization method. Overall, it is shown that the ESN can be a good alternative method for improved lake level forecasting, performing better than both the RNN and the BNN on the four selected Great Lakes time series, namely, the Lakes Erie, Huron-Michigan, Ontario, and Superior.