Prediction of Indoor Air Quality using Long Short-Term Memory with Adaptive Gated Recurrent Unit
There is significant evidence that the COVID-19 virus may be spread by inhaling aerosols leading to risk of infections across indoor environments. Having said that, it is clear that the formulation of indoor air quality (IAQ) needs to be carefully examined. In general, IAQ can be controlled by prope...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2023-01-01
|
| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/33/e3sconf_iaqvec2023_01095.pdf |
| _version_ | 1827921247021826048 |
|---|---|
| author | Rahim Muhamad Sharifuddin Abd Yakub Fitri Omar Mas Ghani Rasli Abd Salim Sheikh Ahmad Zaki Shaikh Masuda Shiro Dhamanti Inge |
| author_facet | Rahim Muhamad Sharifuddin Abd Yakub Fitri Omar Mas Ghani Rasli Abd Salim Sheikh Ahmad Zaki Shaikh Masuda Shiro Dhamanti Inge |
| author_sort | Rahim Muhamad Sharifuddin Abd |
| collection | DOAJ |
| description | There is significant evidence that the COVID-19 virus may be spread by inhaling aerosols leading to risk of infections across indoor environments. Having said that, it is clear that the formulation of indoor air quality (IAQ) needs to be carefully examined. In general, IAQ can be controlled by proper ventilation system across buildings. Nevertheless, different buildings require different mechanistic approaches and it may not be an effective solution for the buildings. Thus, statistical approaches have great potential to evaluate the IAQ in real occupied buildings. Numerous machine learning (ML) techniques were introduced to forecast the indoor environmental risk across buildings. However, there is inadequate data available on how well these ML techniques perform in indoor environments. Recurrent neural network (RNN) is a ML technique that deals with sequential data or time series data. However, the RNN model gradient tends to explode and vanish, leading to inaccurate prediction outcomes. Therefore, this study presents the development of a time based prediction model, Long Short-Term Memory (LSTM) with adaptive gated recurrent units for the prediction of IAQ. Using an advanced LSTM model, the study focuses on the performance of the prediction accuracy and the loss during training and validation. Also, the developed model will be assessed with other RNN models for data validation and comparisons. A set of particulate matter (PM2.5) dataset from commercial buildings is assessed, preprocessed and clean to ensure quality prediction outcomes. This study demonstrates the performance of the hybrid LSTM model to remember past information, minimize gradient error and predict the future data precisely, ensuring a healthier indoor building environment. |
| first_indexed | 2024-03-13T04:24:21Z |
| format | Article |
| id | doaj.art-f47a77aa2ab54d4d917611ea9221f810 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-13T04:24:21Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-f47a77aa2ab54d4d917611ea9221f8102023-06-20T09:04:03ZengEDP SciencesE3S Web of Conferences2267-12422023-01-013960109510.1051/e3sconf/202339601095e3sconf_iaqvec2023_01095Prediction of Indoor Air Quality using Long Short-Term Memory with Adaptive Gated Recurrent UnitRahim Muhamad Sharifuddin Abd0Yakub Fitri1Omar Mas2Ghani Rasli Abd3Salim Sheikh Ahmad Zaki Shaikh4Masuda Shiro5Dhamanti Inge6WEE Laboratory, Malaysia-Japan International Institute of Technology, Universiti Teknologi MalaysiaDepartment of Electronic System Engineering, Malaysia-Japan International Institute of Technology, Universiti Teknologi MalaysiaWEE Laboratory, Malaysia-Japan International Institute of Technology, Universiti Teknologi MalaysiaDepartment of Electronic System Engineering, Malaysia-Japan International Institute of Technology, Universiti Teknologi MalaysiaDepartment of Mechanical Precision Engineering, Malaysia-Japan International Institute of Technology, Universiti Teknologi MalaysiaFaculty of Systems Design and Graduate School of Systems Design, Tokyo Metropolitan UniversityDepartment of Health Policy and Administration, Faculty of Public Health, Universitas AirlanggaThere is significant evidence that the COVID-19 virus may be spread by inhaling aerosols leading to risk of infections across indoor environments. Having said that, it is clear that the formulation of indoor air quality (IAQ) needs to be carefully examined. In general, IAQ can be controlled by proper ventilation system across buildings. Nevertheless, different buildings require different mechanistic approaches and it may not be an effective solution for the buildings. Thus, statistical approaches have great potential to evaluate the IAQ in real occupied buildings. Numerous machine learning (ML) techniques were introduced to forecast the indoor environmental risk across buildings. However, there is inadequate data available on how well these ML techniques perform in indoor environments. Recurrent neural network (RNN) is a ML technique that deals with sequential data or time series data. However, the RNN model gradient tends to explode and vanish, leading to inaccurate prediction outcomes. Therefore, this study presents the development of a time based prediction model, Long Short-Term Memory (LSTM) with adaptive gated recurrent units for the prediction of IAQ. Using an advanced LSTM model, the study focuses on the performance of the prediction accuracy and the loss during training and validation. Also, the developed model will be assessed with other RNN models for data validation and comparisons. A set of particulate matter (PM2.5) dataset from commercial buildings is assessed, preprocessed and clean to ensure quality prediction outcomes. This study demonstrates the performance of the hybrid LSTM model to remember past information, minimize gradient error and predict the future data precisely, ensuring a healthier indoor building environment.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/33/e3sconf_iaqvec2023_01095.pdfindoor air qualitypredictionmachine learninglong short-term memoryhybrid |
| spellingShingle | Rahim Muhamad Sharifuddin Abd Yakub Fitri Omar Mas Ghani Rasli Abd Salim Sheikh Ahmad Zaki Shaikh Masuda Shiro Dhamanti Inge Prediction of Indoor Air Quality using Long Short-Term Memory with Adaptive Gated Recurrent Unit E3S Web of Conferences indoor air quality prediction machine learning long short-term memory hybrid |
| title | Prediction of Indoor Air Quality using Long Short-Term Memory with Adaptive Gated Recurrent Unit |
| title_full | Prediction of Indoor Air Quality using Long Short-Term Memory with Adaptive Gated Recurrent Unit |
| title_fullStr | Prediction of Indoor Air Quality using Long Short-Term Memory with Adaptive Gated Recurrent Unit |
| title_full_unstemmed | Prediction of Indoor Air Quality using Long Short-Term Memory with Adaptive Gated Recurrent Unit |
| title_short | Prediction of Indoor Air Quality using Long Short-Term Memory with Adaptive Gated Recurrent Unit |
| title_sort | prediction of indoor air quality using long short term memory with adaptive gated recurrent unit |
| topic | indoor air quality prediction machine learning long short-term memory hybrid |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/33/e3sconf_iaqvec2023_01095.pdf |
| work_keys_str_mv | AT rahimmuhamadsharifuddinabd predictionofindoorairqualityusinglongshorttermmemorywithadaptivegatedrecurrentunit AT yakubfitri predictionofindoorairqualityusinglongshorttermmemorywithadaptivegatedrecurrentunit AT omarmas predictionofindoorairqualityusinglongshorttermmemorywithadaptivegatedrecurrentunit AT ghanirasliabd predictionofindoorairqualityusinglongshorttermmemorywithadaptivegatedrecurrentunit AT salimsheikhahmadzakishaikh predictionofindoorairqualityusinglongshorttermmemorywithadaptivegatedrecurrentunit AT masudashiro predictionofindoorairqualityusinglongshorttermmemorywithadaptivegatedrecurrentunit AT dhamantiinge predictionofindoorairqualityusinglongshorttermmemorywithadaptivegatedrecurrentunit |