Spatio⁃temporal model of soil moisture prediction integrated with transfer learning

被引：0
|
作者：
Wang X.-Z. [1 ]
Li Q.-L. [2 ]
Li W.-H. [1 ]
机构：
[1] College of Computer Science and Technology, Jilin University, Changchun
[2] School of Computer Science and Technology, Changchun Normal University, Changchun
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2022年 / 52卷 / 03期
关键词：
Computer application; Convolutional neural network; Long short-term memory networks; Soil moisture prediction; Transfer learning;
D O I：
10.13229/j.cnki.jdxbgxb20210608
中图分类号：
学科分类号：
摘要：
Using the deep learning methods can solve the model over-fitting caused by less observation data, and improve the prediction accuracy. This paper proposes spatio-temporal model of soil moisture prediction integrated with transfer learning. Firstly, the EAR5-land dataset is used as the source model. Then three-dimensional layer convolution is used to extract the spatial characteristics of the lag time of the soil moisture, and the long short-time memory network is integrated to extract the temporal characteristics. Third, the network model is pre-trained. Finally, the fine-tune method is applied to adjust the network parameters in the SMAP dataset for soil moisture prediction. The experimental results show that the proposed model has the better prediction results than the convolutional neural network, long short-term memory network and PredRNN. Meanwhile the method of transfer learning can improve the prediction accuracy. © 2022, Jilin University Press. All right reserved.
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页码：675 / 683
页数：8
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