A Temporal Approach for Air Quality Forecast

Eric Hsueh Chan Lu, Chia Yu Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recently, air pollution caused by particulate matter that the diameter is less than or equal to 2.5 μg/m3 has become an important issue. It is so tiny that it can go through alveolar microvascular and enter our body. PM2.5 makes a significant impact on human health. Therefore, monitoring and forecasting the air quality is an indispensable task for human society. Nowadays, we can easily acquire Air Quality Indices (AQIs) by installing a small-scale air quality sensor or downloading from some freely authorized databases. However, people demand farther PM2.5 information to plan their route. This research aims to forecast PM2.5 value in the future hours. Previous studies indicated that the air quality varies nonlinearly in urban areas and depends on several factors such as temperature, humidity and wind speed. Therefore, we combine air quality data from AirBox and meteorology data to forecast PM2.5 value. Air quality is a continuous data. If monitored air quality is good at the last time stamp, the next monitored air quality has high possibility to be good at the same location. And air quality may have some regular in the history data. We forecast PM2.5 values via the algorithm similar to weighted average method. It can figure out the time intervals with similar weather condition. Finally, the error is calculated to examine the accuracy of our method. In contrast to a famous method, Pearson’s Correlation Coefficient, our method preforms well and stable with farther forecast.

Original languageEnglish
Title of host publicationIntelligent Information and Database Systems - 11th Asian Conference, ACIIDS 2019, Proceedings
EditorsTzung-Pei Hong, Ngoc Thanh Nguyen, Bogdan Trawiński, Ngoc Thanh Nguyen, Ford Lumban Gaol
PublisherSpringer Verlag
Pages144-151
Number of pages8
ISBN (Print)9783030148010
DOIs
Publication statusPublished - 2019 Jan 1
Event11th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2019 - Yogyakarta, Indonesia
Duration: 2019 Apr 82019 Apr 11

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11432 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2019
CountryIndonesia
CityYogyakarta
Period19-04-0819-04-11

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Lu, E. H. C., & Liu, C. Y. (2019). A Temporal Approach for Air Quality Forecast. In T-P. Hong, N. T. Nguyen, B. Trawiński, N. T. Nguyen, & F. L. Gaol (Eds.), Intelligent Information and Database Systems - 11th Asian Conference, ACIIDS 2019, Proceedings (pp. 144-151). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11432 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-14802-7_12