基于CNN-LSTM的土壤温室气体排放预测研究  

Study on Prediction of Soil Greenhouse Gas Emission Based on CNN-LSTM

作　　者：李亚蓉 燕振刚[1] 高留玉 LI Yarong;YAN Zhengang;GAO Liuyu(College of Information Science and Technology,Gansu Agricultural University,Lanzhou 730070,China)

机构地区：[1]甘肃农业大学信息科学技术学院,甘肃兰州730070

出　　处：《软件导刊》2025年第3期37-42,共6页Software Guide

基　　金：甘肃省高等学校创新基金项目(2021A-057);甘肃省重点研发计划项目(21YF5FA095)。

摘　　要：为了充分挖掘温室气体排放数据中的时序性特征联系,提高温室气体排放预测精度,提出一种基于CNN-LSTM混合神经网络模型的农田温室气体排放预测方法。以甘肃省陇南市文县大田试验为基础,将实验所测的温度、含水量和全氮等影响因素作为神经网络的输入变量,土壤温室气体排放量作为输出变量,建立温室气体预测的卷积神经网络与长短时记忆神经网络混合模型。结果表明,CNN-LSTM混合神经网络预测模型的相关系数(R^(2)(CO_(2))=0.9242、R^(2)(CH_(4))=0.9556、R^(2)(N_(2)O)=0.9642)、均方根误差(RMSE(CO_(2))=0.0126、RMSE(CH_(4))=0.0153、RMSE(N_(2)O)=0.0330)以及平均绝对误差(MAE(CO_(2))=0.0152、MAE(CH_(4))=0.0115、MAE(N_(2)O)=0.0270)均高于BP人工神经网络模型和LSTM长短期记忆网络模型,说明CNN-LSTM混合神经网络模型能更好地适用于农田温室气体排放预测。In order to fully explore the temporal characteristics in greenhouse gas emission data and improve the accuracy of greenhouse gas emission prediction,a prediction method of farmland greenhouse gas emissions based on CNN-LSTM hybrid neural network model is proposed.Based on the field experiment in Wenxian County,Longnan City,Gansu Province,the influencing factors such as temperature,water content and total nitrogen measured in the experiment were used as the input variables of the neural network,and the emission of soil greenhouse gases was used as the output variables,and a hybrid model of convolutional neural network and long short-term memory neural network for greenhouse gas prediction was established.The results show that the correlation coefficients(R^(2)(CO_(2))=0.9242,R^(2)(CH_(4))=0.9556,R^(2)(N_(2)O)=0.9642),root mean square error(RMSE(CO_(2))=0.0126,RMSE(CH_(4))=0.0153,RMSE(N_(2)O)=0.0330)and mean absolute error(MAE(CO_(2))=0.0152,MAE(CH_(4))=0.0330)and mean absolute error(MAE(CO_(2))=0.0152,MAE(CH_(4))=0.0115 and MAE(N_(2)O)=0.0270)were higher than those of the BP artificial neural network model and the LSTM long short-term memory network model,indicating that the CNN-LSTM hybrid neural network model was more suitable for predicting greenhouse gas emissions from farmland.

关 键 词：农田土壤 温室气体排放 卷积神经网络 长短期记忆网络 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]