Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
5-2013
Abstract
This paper proposes an adaptive Markov chain pattern detection (AMCPD) method for disclosing the climate change patterns of Singapore through meteorological data mining. Meteorological variables, including daily mean temperature, mean dew point temperature, mean visibility, mean wind speed, maximum sustained wind speed, maximum temperature and minimum temperature are simultaneously considered for identifying climate change patterns in this study. The results depict various weather patterns from 1962 to 2011 in Singapore, based on the records of the Changi Meteorological Station. Different scenarios with varied cluster thresholds are employed for testing the sensitivity of the proposed method. The robustness of the proposed method is demonstrated by the results. It is observed from the results that the early weather patterns that were present from the 1960s disappear consistently across models. Changes in temporal weather patterns suggest long-term changes to the climate of Singapore which may be attributed in part to urban development, and global climate change on a larger scale. Our climate change pattern detection algorithm is proven to be of potential use for climatic and meteorological research as well as research focusing on temporal trends in weather and the consequent changes.
Keywords
Meteorological data, Pattern detection, Weather patterns of Singapore, Climate change, Data mining, Incremental Markov chain model
Discipline
Artificial Intelligence and Robotics | Environmental Sciences | Numerical Analysis and Scientific Computing
Research Areas
Intelligent Systems and Optimization
Publication
2013 International Conference on Social Intelligence and Technology (SOCIETY): May 8-10, State College PA: Proceedings
First Page
72
Last Page
79
ISBN
9781479900459
Identifier
10.1109/SOCIETY.2013.15
Publisher
IEEE Computer Society
City or Country
Los Alamitos, CA
Citation
1
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/SOCIETY.2013.15
Included in
Artificial Intelligence and Robotics Commons, Environmental Sciences Commons, Numerical Analysis and Scientific Computing Commons