软件导刊2025，Vol.24Issue(3)：37-42,6.DOI:10.11907/rjdk.241052
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
摘要
Abstract
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 pro-posed.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 green-house 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(R2(CO2)=0.924 2,R2(CH4)=0.955 6,R2(N2O)=0.964 2),root mean square error(RMSE(CO2)=0.012 6,RMSE(CH4)=0.015 3,RMSE(N2O)=0.033 0)and mean absolute error(MAE(CO2)=0.015 2,MAE(CH4)=0.033 0)and mean absolute error(MAE(CO2)=0.0152,MAE(CH4)=0.011 5 and MAE(N2O)=0.027 0)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Key words
agricultural soil/greenhouse gas emission/convolutional neural network/long and short term memory network分类
计算机与自动化引用本文复制引用
李亚蓉,燕振刚,高留玉..基于CNN-LSTM的土壤温室气体排放预测研究[J].软件导刊,2025,24(3):37-42,6.基金项目
甘肃省高等学校创新基金项目(2021A-057) （2021A-057）
甘肃省重点研发计划项目(21YF5FA095) （21YF5FA095）
