Prediction of GHG emissions from Chengdu Metro in the construction stage based on WOA-DELM

Abstract

With the mass construction of urban subways, the global greenhouse gas (GHG) emissions have been on the rise. This paper provides statistical evidence to support the infrastructure of subway emissions reduction through a study of GHG emissions during the construction stage of 6 stations and 7 sections of Chengdu Metro Line 18. Using the emission coefficient method, the GHG emissions from building material production, transportation and site construction in subway stations and shield sections were calculated, and a subway GHG emissions prediction model dependent on deep extreme learning machine (DELM) with whale optimization algorithm (WOA) was established(i.e., WOA-DELM). Compared with some optimized DELMs, namely wind driven optimizer (WDO) -DELM, grey wolf optimizer (GWO) -DELM, particle swarm optimizer (PSO) -DELM, artificial bee colony (ABC) -DELM, multi verse optimizer (MVO) -DELM, and atom search optimizer (ASO) -DELM, and some non-optimized algorithm models, namely back propagation neural network (BPNN), kernel extreme learning machine (KELM) and DELM, the correlation consistency of WOA-DELM algorithm prediction results (0.757) was found to be slightly higher. Through sensitivity analysis of the main input variables of subway GHG emissions with the WOA-DELM algorithm model, it was determined that the key influencing factors of station GHG emissions prediction were the station length and the depth of track surface, with relative change rates of corresponding variables of GHG emissions at 30.1% and 23.1% respectively. Finally, a rough prediction formula of GHG emissions from Chengdu Metro stations and shield sections were fitted based on the key influencing factors of GHG emissions. This study provides a practical and effective reference for reducing GHG emissions in subway construction and operation.