Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (8): 2430-2436.doi: 10.13229/j.cnki.jdxbgxb.20220125

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Prediction of soil moisture based on a deep learning model

Qing-tian GENG1(),Zhi LIU1,Qing-liang LI1(),Fan-hua YU2,Xiao-ning LI1   

  1. 1.College of Computer Science and Technology，Changchun Normal University，Changchun 130032，China
    2.Beihua University，Jilin 132013，China
  • Received:2022-02-11 Online:2023-08-01 Published:2023-08-21
  • Contact: Qing-liang LI E-mail:qtgeng@mail.ccsfu.edu.cn;lliqingliang@ccsfu.edu.cn

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Abstract:

The current deep learning-based hydrological prediction models mainly use the same network weights to simulate the spatio-temporal relationship between soil moisture and predictors， which has some limitations in reflecting the spatio-temporal specificity of soil moisture. In this paper， we propose a spatio-temporal prediction model with channel-agnostic and spatial-specific structures. Spatial-specific features are used to extract the spatial characteristics of soil moisture in different regions by adaptively assigning network weights at different spatial locations； channel-agnostic features are used to extract the spatial characteristics of soil moisture and predict in a single grid point channel using one-dimensional convolution. The channel-agnostic feature extracts the temporal characteristics of soil moisture and predict in a single grid point channel using one-dimensional convolution， accurately captures the transformed relationship between soil moisture and its predictor at a single point， and uses lagged and predictor as model inputs for prediction at a single grid point. The experimental results show that the prediction ability of the model in this paper is greatly improved compared with the traditional DL model， and most obviously in the northern region of China， the determination coefficient （R2） of the model is improved by 80% compared with that of CNN.

Key words: software engineering, soil moisture, deep learning, machine learning

CLC Number: 

  • TP391

Fig.1

Average SMAP soil moisture in North China"

Fig.2

Average SMAP soil moisture in Central China"

Fig.3

Average SMAP soil moisture in Southwest China"

Fig.4

Average SMAP soil moisture in East China"

"

区域数据最小值最大值平均中值标准差
华北SMAP0.01260.46700.14330.13890.0502
华中SMAP0.01210.48700.12290.11120.0535
华东SMAP0.02870.72560.25830.26050.0772
西南SMAP0.00790.72600.17490.15840.0809

Fig.5

Average Convolution kernel generation and data processing process"

Table 2

R2 statistics of experimental results in four regions of China."

区域CNN 模型LSTM 模型ConvLSTM 模型本文 模型
华北0.4770.5970.6520.870
华中0.1930.1970.3190.401
西南0.2310.1620.2840.459
华中0.4750.5180.5450.593

Fig.6

DL model training loss map"

1 刘卉, 汪懋华, 王跃宣, 等. 基于无线传感器网络的农田土壤温湿度监测系统的设计与开发[J]. 吉林大学学报: 工学版, 2008, 38(3): 604-608.
Liu Hui, Wang Mao-hua, Wang Yue-xuan, et al. Development of farmland soil moisture and temperature monitoring system based on wireless sensor network[J]. Journal of Jilin University(Engineering and Technology Edition), 2008, 38(3): 604-608.
2 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.
Wang Xue-zhi, Li Qing-liang, Li Wen-hui, et al. 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.
3 Dirmeye P A, Gao X, Zhao M, et al. Multimodel analysis and implications for our perception of the land surface[J]. Bulletin of the American Meteorological Society, 2006, 87: 1381-1397.
4 Fang K, Shen C, Kifer D, et al. Prolongation of SMAP to spatiotemporally seamless coverage of continental U.S. using a deep learning neural network[J]. Geophysical Research Letters, 2017, 44(11): 30-39.
5 Kalakuntla R, Wille T, Provost R L, et al. Analysis of the linearised observation operator in a land surface data assimilation scheme for numerical weather prediction[J]. Toxicology Letters, 2013, 216: 200-205.
6 Markus R, Gustau CV, Bjorn S, et al. Deep learning and process understanding for data-driven Earth system science[J]. Nature,2019, 566: 195.
7 LeCun Y, Bengio Y. Convolutional networks for images, speech, and time series[J]. The Handbook of Brain Theory and Neural Networks, 1995, 3361: 255-258.
8 Hochreiter S, Schmidhuber J, Long short-term memory [J]. Neural Computation,1997, 9: 1735-1780.
9 Shi X J, Chen Z R, Wang H, et al. Convolutional LSTM network: a machine learning approach for precipitation nowcasting[J]. In Advances in Neural Information Processing Systems, 2015,28: 802-810.
10 Sobayo R, Wu H H, Ray R, et al. Improving precipitation estimation using convolutional neural network[J]. Water Resources Research, 2019, 55: 2301-2321.
11 Zhao Q M, Song Z T, et al. Research on multi-point temperature and humidity prediction method of mushroom room based on CNN-GRU[J]. Transactions of The Chinese Society of Agricultural Machinery, 2020, 51: 1000-1298.
12 Fang K, Shen C,Near-real-time forecast of satellite-based soil moisture using long short-term memory with an adaptive data integration kernel[J]. Journal of Hydrometeorology, 2020, 21: 399-413.
13 Li Q, Zhao Y, Yu F, A novel multichannel long short-term memory method with time series for soil temperature modeling[J]. IEEE Access, 2020(8): 182026-182043.
14 Mohamed E, Emad H, Ahmed M. A, et al. Assessment of a spatiotemporal deep learning approach for soil moisture prediction and filling the gaps in between soil moisture observations[J]. Frontiers in Artificial Intelligence, 2021(4): 636234.
15 Kumar S. V, Reichle R. H, Koster R. D,et al. Role of subsurface physics in the assimilation of surface soil moisture observations[J]. Journal of Hydrometeorology, 2009, 10: 1534-1547.
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