Indoor Air Wellness: A Predictive Model for Pollution Control Using Advanced AI Techniques

Indoor Air Wellness: A Predictive Model for Pollution Control Using Advanced AI Techniques

Indoor air quality (IAQ) has a crucial impact on health, yet many spaces suffer from unnoticed pollution. This study introduces “Indoor Air Wellness”, a predictive model that utilizes advanced AI techniques, specifically integrating Kalman filtering and artificial neural networ...

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Main Authors: Kalyan Chatterjee, Muntha Raju, Bhoomeshwar Bala, U. Nikitha, Gampala Prabhas, Naim Ahmad, Ayman Qahmash, Wade Ghribi, Bernardo Lemos, Saurav Mallik
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Artificial neural network
control mechanism
deep learning
indoor air pollution
indoor air quality
Kalman filter
Online Access:https://ieeexplore.ieee.org/document/10909552/
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Summary:Indoor air quality (IAQ) has a crucial impact on health, yet many spaces suffer from unnoticed pollution. This study introduces &#x201C;Indoor Air Wellness&#x201D;, a predictive model that utilizes advanced AI techniques, specifically integrating Kalman filtering and artificial neural networks, to enhance indoor air pollution prediction and management. The model excels in dynamic environments by refining the accuracy of the proposed Kal-ANN algorithm achieving a predictive accuracy of up to 96. 55%, a root mean square error, a Mean Absolute Error, and a Mean Squared Prediction Error as low as 9.85, 6.12, and <inline-formula> <tex-math notation="LaTeX">$3.15~g/m$ </tex-math></inline-formula>. &#x201C;Indoor Air Wellness&#x201D; operates in three phases: IAQ prediction, control, and energy-efficient green building design. IAQ prediction consists of Learning and Analysis subphases, continuously monitoring and forecasting indoor parameters such as temperature, humidity, and CO levels while considering external factors. The Analysis phase implements control measures based on these predictions, leading to a 60% improvement in energy utilization in green buildings compared to traditional methods. Simulations and real-world applications demonstrate its effectiveness in reducing pollutants by up to 23% and minimizing energy consumption by up to 48%. The &#x201C;Indoor Air Wellness&#x201D; model enhances air quality and promotes energy efficiency, positioning it as a valuable tool for residential, commercial, and industrial applications. This study contributes to advancing smart environmental management, advocating for healthier indoor environments through AI-driven solutions.
ISSN:2169-3536

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