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.
Highlights
Water level plays an important part in the community’s well-being and economic livelihoods.For example, water level changes can impact physical processes in lakes, such as circulation, resulting in changes in water mixing and bottom sediment resuspension, and could further affect water quality and aquatic ecosystems [1,2]
The performance of machine learning (ML) models has 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 multiple linear regression (MLR) and M5P, can be used for forecasting Lake Erie water levels and informing future water resources management
Summary
Water level changes can impact physical processes in lakes, such as circulation, resulting in changes in water mixing and bottom sediment resuspension, and could further affect water quality and aquatic ecosystems [1,2]. Water level prediction attracts more and more attention [3,4]. The International Joint Commission (IJC) suggests more efforts should be implemented to improve the methods of monitoring and predicting water level [5]. Water-level change is a complex hydrological phenomenon due to its various controlled factors, including meteorological conditions, as well as water exchange between the lake and its watersheds [6,7]. Many tools used to forecast water levels, while considering influencing factors, have been developed, such as process-based models [8]. Gronewold et al showed that the advanced hydrologic prediction system (AHPS) can be used to capture seasonal and inter-annual patterns of
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