Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)

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A Two-Stage XGBoost Pipeline for Environmental Parameter and AQI Forecasting in a Smart Indoor Air Quality Monitoring System

Authors
Deepali S. Jadhav1, *, Parth Supekar1, Aditya Patil1, Mandar Patil1, Lalit Patil1, Reet Parmar1
1Vishwakarma Institute of Technology, Pune, 411037, Maharashtra, India
*Corresponding author. Email: deepali.jadhav@vit.edu
Corresponding Author
Deepali S. Jadhav
Available Online 6 January 2026.
DOI
10.2991/978-94-6463-948-3_18How to use a DOI?
Keywords
Predictive Analytics; XGBoost Regressor; MultiOutputRegressor; AQI Forecasting; Sensor Networks
Abstract

With more than 80% of life indoors, Indoor Air Quality (IAQ) issues constitute a major health concern. Existing Internet of Things (IoT) and Machine Learning (ML) monitoring frameworks almost exclusively utilize one-stage models that estimate the Air Quality Index (AQI) from raw sensor measurements, a process vulnerable to noise and narrow in precision. In response, we suggest a new two-stage predictive pipeline. The first stage employs a MultiOutputRegressor with an XGBoost estimator to predict the main environmental parameters (e.g., CO, temperature, humidity), which are used as model-refined engineered features for a second, standalone XGBoost model predicting the final AQI. Our two-step XGBoost-based framework had a Mean Absolute Error (MAE) of 0.35, a Root Mean Square Error (RMSE) of 0.54, and an R2 of 1.00 for AQI prediction, far outperforming the conventional single-step baseline models. This validating the use of model-enhanced features offers a more precise and reliable framework toward more reliable, anticipatory, and intelligent environment sensing in smart living spaces.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
Series
Advances in Intelligent Systems Research
Publication Date
6 January 2026
ISBN
978-94-6463-948-3
ISSN
1951-6851
DOI
10.2991/978-94-6463-948-3_18How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Deepali S. Jadhav
AU  - Parth Supekar
AU  - Aditya Patil
AU  - Mandar Patil
AU  - Lalit Patil
AU  - Reet Parmar
PY  - 2026
DA  - 2026/01/06
TI  - A Two-Stage XGBoost Pipeline for Environmental Parameter and AQI Forecasting in a Smart Indoor Air Quality Monitoring System
BT  - Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
PB  - Atlantis Press
SP  - 255
EP  - 264
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-948-3_18
DO  - 10.2991/978-94-6463-948-3_18
ID  - Jadhav2026
ER  -