id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
ajst-30905	Li, Jiaoyuan	Syngas Calorific Value Prediction for Underground Coal Gasification Based on Informer	2025	5	.pdf	application/pdf	3949	234	42	[20] proposed a dual-source long short-term memory (LSTM) prediction model to for predicting UCG statuses and achieved better prediction accuracy than that of the existing methods, with a maximum equivalence trend prediction accuracy of 90.99%. Compared to theoretical model predictions approaches, experimental data prediction models for UCG enable online real-time prediction capabilities and offer greater accuracy.	cache/ajst-30905.pdf	txt/ajst-30905.txt