Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm
| Autor: | Nuraminah Ramli, Xiaoyang Mao, Syed Muhammad Mamduh Syed Zakaria, E. Kanagaraj, Hiromitsu Nishizaki, Ammar Zakaria, Md. Fauzan Elham, Abdul Syafiq Abdull Sukor, C.C. Goh, Latifah Munirah Kamarudin |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
internet of things (IoT)
Coefficient of determination 010504 meteorology & atmospheric sciences Mean squared error Computer science in-vehicle air quality smart mobility TP1-1185 010501 environmental sciences Machine learning computer.software_genre machine learning prediction 01 natural sciences Biochemistry Article Analytical Chemistry Machine Learning Air Pollution Linear regression Electrical and Electronic Engineering Instrumentation Air quality index 0105 earth and related environmental sciences Measure (data warehouse) business.industry Chemical technology Predictive analytics Atomic and Molecular Physics and Optics Support vector machine smart city Multilayer perceptron Particulate Matter Neural Networks Computer Artificial intelligence business Algorithm computer Algorithms |
| Zdroj: | Sensors (Basel, Switzerland) Sensors, Vol 21, Iss 4956, p 4956 (2021) Sensors Volume 21 Issue 15 |
| ISSN: | 1424-8220 |
| Popis: | This paper presents the development of a real-time cloud-based in-vehicle air quality monitoring system that enables the prediction of the current and future cabin air quality. The designed system provides predictive analytics using machine learning algorithms that can measure the drivers’ drowsiness and fatigue based on the air quality presented in the cabin car. It consists of five sensors that measure the level of CO2, particulate matter, vehicle speed, temperature, and humidity. Data from these sensors were collected in real-time from the vehicle cabin and stored in the cloud database. A predictive model using multilayer perceptron, support vector regression, and linear regression was developed to analyze the data and predict the future condition of in-vehicle air quality. The performance of these models was evaluated using the Root Mean Square Error, Mean Squared Error, Mean Absolute Error, and coefficient of determination (R2). The results showed that the support vector regression achieved excellent performance with the highest linearity between the predicted and actual data with an R2 of 0.9981. |
| Databáze: | OpenAIRE |
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