Research on Carbon Emission Prediction of Airport Terminal Area Based on GRA-LSTM
LUO Jun
WU Xiaolin
ZHU Ziyao
DU Yuxin
HAN Xiaotong
WU Yulei
Abstract：This paper aims to forecast the carbon emission of airport terminal area by integrating grey correlation analysis and long short term memory network(LSTM)method.Firstly,grey correlation degree analysis is used to identify and quantify the corre-lation degree between carbon emissions and various influencing factors in the airport terminal area,so as to screen out the key fac-tors that have a significant impact on carbon emissions.Then,based on the selected key factors,a carbon emission prediction model based on LSTM is constructed.Through the data of 11 airports in the past six years,the LSTM model predicts the future of airports.
Keywords：LSTM neural networkgrey correlation analysiscarbon emission prediction
Publication Date：2025-10-20
Online Publishing Date：2026-01-16(First online date of this platform, not the publication date of the document)
Pages：5( 91-94,163 )