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.

中文翻译：

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基于机器学习的伊利湖水位预测

预测伊利湖的水位在水资源管理和航行中很重要，因为水位会显着影响货物运输选择以及个人娱乐活动选择。本文应用了机器学习（ML）算法，包括高斯过程（GP）、多元线性回归（MLR）、多层感知器（MLP）、M5P模型树、随机森林（RF）和k-最近邻（KNN）预测伊利湖的水位。2002-2014年气象数据和前一天观测水位为自变量，日水位为因变量。预测结果表明，MLR 和 M5P 在均方根误差 (RMSE) 和平均绝对误差 (MAE) 方面具有最高的准确度。ML 模型的性能还与基于过程的高级水文预测系统 (AHPS) 的性能进行了比较，结果表明 ML 模型与 AHPS 相比在预测精度上更为优越。连同其节省时间的优势，本研究表明 ML 模型，尤其是 MLR 和 M5P，可用于预测伊利湖水位并为未来的水资源管理提供信息。