融合迁移学习的土壤湿度预测时空模型

doi:10.13229/j.cnki.jdxbgxb20210608

吉林大学学报(工学版) ›› 2022, Vol. 52 ›› Issue (3): 675-683.doi: 10.13229/j.cnki.jdxbgxb20210608

• 计算机科学与技术 • 上一篇

融合迁移学习的土壤湿度预测时空模型

王学智¹(),李清亮²,李文辉¹()

^1.吉林大学计算机科学与技术学院，长春 130012
^2.长春师范大学计算机科学与技术学院，长春 130032

收稿日期:2021-07-04 出版日期:2022-03-01 发布日期:2022-03-08
通讯作者: 李文辉 E-mail:wxz14@mails.jlu.edu.cn;liwh@mails.jlu.edu.cn
作者简介:王学智（1987-），男，博士研究生. 研究方向：计算机应用技术.E-mail：wxz14@mails.jlu.edu.cn
基金资助:
国家自然科学基金项目(51805203);吉林省科技厅发展计划项目(20190201023JC);吉林省发改委项目(2019C054-2)

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

Xue-zhi WANG¹(),Qing-liang LI²,Wen-hui LI¹()

^1.College of Computer Science and Technology，Jilin University，Changchun 130012，China
^2.School of Computer Science and Technology，Changchun Normal University，Changchun 130032，China

Received:2021-07-04 Online:2022-03-01 Published:2022-03-08
Contact: Wen-hui LI E-mail:wxz14@mails.jlu.edu.cn;liwh@mails.jlu.edu.cn

摘要/Abstract

摘要：

针对土壤湿度观测数据量过少导致模型出现过拟合而影响预测精度的问题，本文提出了融合迁移学习的土壤湿度预测时空模型。首先，将ERA5-land数据集作为源域。然后，通过三维卷积层提取土壤湿度滞后时刻的空间特征，并融入长短期记忆网络提取其时间特征，对网络模型进行预训练。最后，以微调方式在SMAP数据集中调整网络参数，进而预测未来土壤湿度。实验结果表明，本文提出的时空深度学习模型相对于卷积神经网络、长短期记忆网络和PredRNN时空预测模型预测精度更高，同时通过迁移学习方法可以进一步提升模型的预测精度。

关键词: 计算机应用, 土壤湿度预测, 卷积神经网络, 长短期记忆网络, 迁移学习

Abstract:

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.

Key words: computer application, soil moisture prediction, convolutional neural network, long short-term memory networks, transfer learning

中图分类号:

TP391

王学智,李清亮,李文辉. 融合迁移学习的土壤湿度预测时空模型[J]. 吉林大学学报(工学版), 2022, 52(3): 675-683.

Xue-zhi WANG,Qing-liang LI,Wen-hui LI. Spatio⁃temporal model of soil moisture prediction integrated with transfer learning[J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(3): 675-683.

图/表 9

图1

图2

表 1

不同的超参数对本文模型的影响"

学习率	batch size	迭代次数	$R 2$
0.001	128	50	0.873
0.01	128	50	0.864
0.0001	128	50	0.868
0.001	64	50	0.869
0.001	256	50	0.862
0.001	128	100	0.865
0.001	128	25	0.868

表 1

图3

图4

图5

图6

图7

表2

参考文献 23

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模型	RMSE	MAE	MSE	R²
CNN	0.049	0.628	1.083	0.871
LSTM	0.047	0.601	0.997	0.890
PredRNN	0.041	0.416	0.648	0.902
冻结卷积层冻结LSTM	0.045 0.047	0.489 0.512	0.695 0.728	0.900 0.898
3DCNN-LSTM	0.039	0.393	0.582	0.913

融合迁移学习的土壤湿度预测时空模型

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

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