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"

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