Interpretable vs. noninterpretable machine learning models for data-driven hydro-climatological process modeling
作者：
Highlights：
• Interpretable machine learning is as accurate as deep learning for structured data.
• Explainable machine learning unveils the underlying physical processes.
• Sequential transfer-learning technique effectively imputes continuous missing data.
摘要
•Interpretable machine learning is as accurate as deep learning for structured data.•Explainable machine learning unveils the underlying physical processes.•Sequential transfer-learning technique effectively imputes continuous missing data.
论文关键词：Deep learning,Boosting,Transfer learning,Hydroclimate,Reference crop evapotranspiration,Model explainability
论文评审过程：Received 28 May 2020, Revised 11 December 2020, Accepted 16 December 2020, Available online 24 December 2020, Version of Record 14 January 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.114498
