Explaining Great Lakes water level variability through interpretable ensemble machine learning

Rahim Barzegar

doi:10.1016/j.scitotenv.2025.181302

2026-01-03

PRE

Rahim Barzegar *

Ehsan Raei

Jan Adamowski

DOI：10.1016/j.scitotenv.2025.181302

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摘要

En 中文

• 基于提升法的机器学习模型显著提高了大湖区水位预测的准确性。 • SHAP–VARS框架识别了主要驱动因素并揭示了它们的滞后敏感性。 • 径流和入流主导苏必利尔湖和密歇根湖，而入流在伊利湖中起决定性驱动作用。 • 温度和蒸发对长期滞后效应具有显著影响，尤其是在安大略湖。

期刊

Science of The Total Environment

IF:

论文数:

7.1W

被引数:

46.4W

机构

University of Quebec in Abitibi-Temiscamingue

学者数:

论文数: 7

被引数: 0

McGill University

学者数:

5.5W

论文数: 4.9W

被引数: 7.0W

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