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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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10909552/ |
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| author | Kalyan Chatterjee Muntha Raju Bhoomeshwar Bala U. Nikitha Gampala Prabhas Naim Ahmad Ayman Qahmash Wade Ghribi Bernardo Lemos Saurav Mallik |
| author_facet | Kalyan Chatterjee Muntha Raju Bhoomeshwar Bala U. Nikitha Gampala Prabhas Naim Ahmad Ayman Qahmash Wade Ghribi Bernardo Lemos Saurav Mallik |
| author_sort | Kalyan Chatterjee |
| collection | DOAJ |
| description | 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 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>. “Indoor Air Wellness” 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 “Indoor Air Wellness” 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. |
| format | Article |
| id | doaj-art-2139ea9eccfa410d9d5c62034493e389 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-2139ea9eccfa410d9d5c62034493e3892025-08-20T02:47:48ZengIEEEIEEE Access2169-35362025-01-0113420994211510.1109/ACCESS.2025.354780810909552Indoor Air Wellness: A Predictive Model for Pollution Control Using Advanced AI TechniquesKalyan Chatterjee0https://orcid.org/0000-0002-9787-3689Muntha Raju1Bhoomeshwar Bala2U. Nikitha3Gampala Prabhas4Naim Ahmad5https://orcid.org/0000-0002-4424-4758Ayman Qahmash6https://orcid.org/0000-0003-2558-9475Wade Ghribi7https://orcid.org/0000-0002-1221-8010Bernardo Lemos8Saurav Mallik9https://orcid.org/0000-0003-4107-6784Department of Computer Science and Engineering, Nalla Malla Reddy Engineering College, Hyderabad, IndiaDepartment of Computer Science and Engineering, Nalla Malla Reddy Engineering College, Hyderabad, IndiaDepartment of Computer Science and Engineering, Nalla Malla Reddy Engineering College, Hyderabad, IndiaDepartment of Computer Science and Engineering, Nalla Malla Reddy Engineering College, Hyderabad, IndiaDepartment of Computer Science and Engineering, Nalla Malla Reddy Engineering College, Hyderabad, IndiaCollege of Computer Science, King Khalid University, Abha, Saudi ArabiaCollege of Computer Science, King Khalid University, Abha, Saudi ArabiaCollege of Computer Science, King Khalid University, Abha, Saudi ArabiaDepartment of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, USADepartment of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, USAIndoor 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 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>. “Indoor Air Wellness” 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 “Indoor Air Wellness” 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.https://ieeexplore.ieee.org/document/10909552/Artificial neural networkcontrol mechanismdeep learningindoor air pollutionindoor air qualityKalman filter |
| spellingShingle | Kalyan Chatterjee Muntha Raju Bhoomeshwar Bala U. Nikitha Gampala Prabhas Naim Ahmad Ayman Qahmash Wade Ghribi Bernardo Lemos Saurav Mallik Indoor Air Wellness: A Predictive Model for Pollution Control Using Advanced AI Techniques IEEE Access Artificial neural network control mechanism deep learning indoor air pollution indoor air quality Kalman filter |
| title | Indoor Air Wellness: A Predictive Model for Pollution Control Using Advanced AI Techniques |
| title_full | Indoor Air Wellness: A Predictive Model for Pollution Control Using Advanced AI Techniques |
| title_fullStr | Indoor Air Wellness: A Predictive Model for Pollution Control Using Advanced AI Techniques |
| title_full_unstemmed | Indoor Air Wellness: A Predictive Model for Pollution Control Using Advanced AI Techniques |
| title_short | Indoor Air Wellness: A Predictive Model for Pollution Control Using Advanced AI Techniques |
| title_sort | indoor air wellness a predictive model for pollution control using advanced ai techniques |
| topic | Artificial neural network control mechanism deep learning indoor air pollution indoor air quality Kalman filter |
| url | https://ieeexplore.ieee.org/document/10909552/ |
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