吉林大学学报(工学版) ›› 2023, Vol. 53 ›› Issue (8): 2430-2436.doi: 10.13229/j.cnki.jdxbgxb.20220125

• 农业工程·仿生工程 • 上一篇    

基于一种深度学习模型的土壤湿度预测

耿庆田1(),刘植1,李清亮1(),于繁华2,李晓宁1   

  1. 1.长春师范大学 计算机科学与技术学院，长春 130032
    2.北华大学，吉林省 吉林市 132013
  • 收稿日期:2022-02-11 出版日期:2023-08-01 发布日期:2023-08-21
  • 通讯作者: 李清亮 E-mail:qtgeng@mail.ccsfu.edu.cn;lliqingliang@ccsfu.edu.cn
  • 作者简介:耿庆田（1972-），男，教授，博士.研究方向：智能信息系统.E-mail：qtgeng@mail.ccsfu.edu.cn
  • 基金资助:
    吉林省科技发展计划项目(20230201070GX)

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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摘要：

针对传统基于深度学习的水文预测模型在反映土壤水分时空特异性有一定局限性的问题，提出了一种具有channel-agnostic和spatial-specific结构的时空预测深度学习模型。该模型通过spatial-specific性质在不同空间位置自适应分配网络权值提取不同区域土壤湿度的空间特征；通过channel-agnostic性质用一维卷积提取单个grid点通道中土壤湿度和predict的时间特征，准确捕获单点的土壤湿度和其预测因子的变换关系，将lagged和预测因子作为模型输入在单个网格点进行预测。实验结果表明：本文模型的预测能力相较于传统DL模型有很大提升，在中国北方区域，模型决定系数（R2）相较于卷积神经网络（CNN）提高了80%。

关键词: 软件工程, 土壤湿度, 深度学习, 机器学习

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

中图分类号: 

  • TP391

图1

华北地区SMAP土壤湿度时间平均值"

图2

华中地区SMAP土壤湿度时间平均值"

图3

西南地区SMAP土壤湿度时间平均值"

图4

华东地区SMAP土壤湿度时间平均值"

表1

不同地区SMAP土壤水分（m3/m3）的统计特征 (Statistical characteristics of SMAP soil moisture （m3/m3） in different regions.)"

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

图5

卷积核生成及处理数据过程"

表2

中国4个实验区域R2数值"

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

图6

DL模型训练损失图"

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