TY - GEN
T1 - Comparative Analysis of Time Series and Machine Learning Models for Air Quality Prediction Utilizing IoT Data
AU - Vonitsanos, Gerasimos
AU - Panagiotakopoulos, Theodor
AU - Kameas, Achilles
N1 - Publisher Copyright:
© IFIP International Federation for Information Processing 2024.
PY - 2024
Y1 - 2024
N2 - Air pollution has been shown to have serious negative effects on people’s health, the environment, and the economy. It is becoming more and more crucial to model, predict, and monitor air quality, particularly in urban areas. Air quality prediction is challenging because of the dynamic nature, instability, and high spatial and temporal variability of particles and pollutants. Internet of things technologies and machine learning offer an efficient way to address these challenges and enables the implementation of effective air quality prediction models. This paper aims to provide a comparative analysis of time series and machine learning methods for air quality prediction based on data collected through IoT sensors. These methods have been evaluated for PM10, PM2.5, and Air Quality Index (AQI) particles. The results indicate that while deep learning models (LSTM) perform better for the air quality index, ARIMA and SVM algorithms best predict the concentrations of the researched air pollutants (PM2.5, PM10).
AB - Air pollution has been shown to have serious negative effects on people’s health, the environment, and the economy. It is becoming more and more crucial to model, predict, and monitor air quality, particularly in urban areas. Air quality prediction is challenging because of the dynamic nature, instability, and high spatial and temporal variability of particles and pollutants. Internet of things technologies and machine learning offer an efficient way to address these challenges and enables the implementation of effective air quality prediction models. This paper aims to provide a comparative analysis of time series and machine learning methods for air quality prediction based on data collected through IoT sensors. These methods have been evaluated for PM10, PM2.5, and Air Quality Index (AQI) particles. The results indicate that while deep learning models (LSTM) perform better for the air quality index, ARIMA and SVM algorithms best predict the concentrations of the researched air pollutants (PM2.5, PM10).
KW - Air Quality Prediction
KW - Deep Learning Models
KW - Machine Learning Algorithms
KW - Time Series Analysis
KW - Urban Air Pollution
UR - http://www.scopus.com/inward/record.url?scp=85199134987&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-63227-3_15
DO - 10.1007/978-3-031-63227-3_15
M3 - Conference contribution
AN - SCOPUS:85199134987
SN - 9783031632266
T3 - IFIP Advances in Information and Communication Technology
SP - 221
EP - 235
BT - Artificial Intelligence Applications and Innovations. AIAI 2024 IFIP WG 12.5 International Workshops - MHDW 2024, 5G-PINE 2024, and AI4GD 2024, Proceedings
A2 - Maglogiannis, Ilias
A2 - Iliadis, Lazaros
A2 - Karydis, Ioannis
A2 - Papaleonidas, Antonios
A2 - Chochliouros, Ioannis
PB - Springer Science and Business Media Deutschland GmbH
T2 - 13th Mining Humanistic Data Workshop, MHDW 2024, 9th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st Workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 International Workshops on Artificial Intelligence Applications and Innovations, AIAI 2024
Y2 - 27 June 2024 through 30 June 2024
ER -
