Back to Search
Start Over
Predictive Analysis of In-Vehicle Air Quality Monitoring System Using Deep Learning Technique.
- Source :
-
Atmosphere . Oct2022, Vol. 13 Issue 10, pN.PAG-N.PAG. 16p. - Publication Year :
- 2022
-
Abstract
- In-vehicle air quality monitoring systems have been seen as promising paradigms for monitoring drivers' conditions while they are driving. This is because some in-vehicle cabins contain pollutants that can cause drowsiness and fatigue to drivers. However, designing an efficient system that can predict in-vehicle air quality has challenges, due to the continuous variation in parameters in cabin environments. This paper presents a new approach, using deep learning techniques that can deal with the varying parameters inside the vehicle environment. In this case, two deep learning models, namely Long-short Term Memory (LSTM) and Gated Recurrent Unit (GRU) are applied to classify and predict the air quality using time-series data collected from the built-in sensor hardware. Both are compared with conventional methods of machine learning models, including Support Vector Regression (SVR) and Multi-layer Perceptron (MLP). The results show that GRU has an excellent prediction performance with the highest coefficient of determination value (R2) of 0.97. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20734433
- Volume :
- 13
- Issue :
- 10
- Database :
- Academic Search Index
- Journal :
- Atmosphere
- Publication Type :
- Academic Journal
- Accession number :
- 159868368
- Full Text :
- https://doi.org/10.3390/atmos13101587