Machine Learning-Based Water Level Prediction in Lake Erie

Abstract

Predicting water levels of Lake Erie is important in water resource management as well as navigation since water level significantly impacts cargo transport options as well as personal choices of recreational activities. In this paper, machine learning (ML) algorithms including Gaussian process (GP), multiple linear regression (MLR), multilayer perceptron (MLP), M5P model tree, random forest (RF), and k-nearest neighbor (KNN) are applied to predict the water level in Lake Erie. From 2002 to 2014, meteorological data and one-day-ahead observed water level are the independent variables, and the daily water level is the dependent variable. The predictive results show that MLR and M5P have the highest accuracy regarding root mean square error (RMSE)  and mean absolute error (MAE). The performance of ML models has also been compared against the performance of the process-based advanced hydrologic prediction system (AHPS), and the results indicate that ML models are superior in predictive accuracy compared to AHPS. Together with their time-saving advantage, this study shows that ML models, especially MLR and M5P, can be used for forecasting Lake Erie water levels and informing future water resources management.