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THERMAL SCIENCE
International Scientific Journal
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FORECASTING INDOOR NO2 CONCENTRATIONS IN A STUDENT DORMITORY USING RNN-LSTM MODELS
ABSTRACT
Air quality has a profound impact on the urban environment, with both positive and negative effects. Developing effective strategies for improving and predicting air quality is crucial for urban environmental management. This study was conducted from June 2021 to March 2022, with a sample size of 584. The study collected air quality data from dormitory buildings, including indoor temperature, wind speed, humidity, and NO2 concentration. Additionally, we conducted a qualitative analysis to explore the relationship between atmospheric parameters and average NO2 concentration. We compared multilayer perceptron with recurrent neural networks (RNN) and long short-term memory (LSTM) models for optimization in predicting NO2 concentrations. Experimental results showed that the combination of RNN and LSTM significantly improved the accuracy of NO2 concentration predictions. This study provides important references for monitoring NO2 concentrations in university dormitories and improving air quality.
KEYWORDS
PAPER SUBMITTED: 2025-06-02
PAPER REVISED: 2025-07-25
PAPER ACCEPTED: 2025-08-01
PUBLISHED ONLINE: 2025-09-13
DOI REFERENCE: https://doi.org/10.2298/TSCI250602161X
CITATION EXPORT: view in browser or download as text file
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© 2026 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence