Interpretable spatio-temporal modeling for soil temperature prediction

Interpretable spatio-temporal modeling for soil temperature prediction

Soil temperature (ST) is a crucial parameter in Earth system science. Accurate ST predictions provide invaluable insights; however, the “black box” nature of many deep learning approaches limits their interpretability. In this study, we present the Encoder-Decoder Model with Interpretable Spatio-Tem...

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Bibliographic Details
Main Authors: Xiaoning Li, Yuheng Zhu, Qingliang Li, Hongwei Zhao, Jinlong Zhu, Cheng Zhang
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-12-01
Series:Frontiers in Forests and Global Change
Subjects:
soil temperature
deep learning
interpretable model
machine learning (ML)
LSTM (long short term memory networks)
Online Access:https://www.frontiersin.org/articles/10.3389/ffgc.2023.1295731/full

Internet

https://www.frontiersin.org/articles/10.3389/ffgc.2023.1295731/full

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