Predicting Water Level Fluctuations in Lake Michigan-Huron Using Wavelet-Expert System Methods

Abstract

Understanding and forecasting water level fluctuations in Lake Michigan-Huron is important for a variety of water resource management operations such as flood control, local water supply management, shoreline maintenance, ecosystem sustainability, recreation, and economic development. In this study, wavelet transform, fuzzy logic and multilayer perceptron techniques are combined to obtain new approaches for forecasting lake level fluctuation. The wavelet approach is used to decompose water level time series into its spectral bands. Predictive models have been developed as stand-alone fuzzy logic, stand-alone multilayer perceptron combined wavelet-fuzzy and combined wavelet-multilayer perceptron models in order to forecast the water level fluctuations. The models were tested to predict the current water level (at t monthly time step) and lead times including t + 3, t + 6, t + 9 and t + 12 time steps from the water levels at two previous time steps (t − 2 and t − 1). In this study, the historic water level data was obtained from Lake Michigan-Huron for the period between 1855 and 2006. For the model development, monthly water level data was divided into two groups. The training group consists of the data for the first 101 years (from 1855 to 1955) with 1212 data points, which were, then, used to predict the water levels for remaining 51 years (from 1956 to 2006). The results reveal that all the four models can predict the water levels quite accurately. In comparison, the combined wavelet-fuzzy logic and combined wavelet-multilayer perceptron models outperformed the stand-alone fuzzy and multilayer perceptron models for lead times of 1, 3, 6, 9 and 12 months. This comparison was performed based on the root mean squared error (RMSE), the coefficient of efficiency (CE), the mean absolute deviation (MAD) and the skill score (SS) between observed data and prediction results.