Disclosing Climate Change Patterns Using an Adaptive Markov Chain Pattern Detection Method

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.

[1]  Connie M. Borror,et al.  Robustness of the Markov-chain model for cyber-attack detection , 2004, IEEE Transactions on Reliability.

[2]  Johan Tykesson.  On the visibility in well-behaved random sets in Euclidean space , 2011 .

[3]  H. L. Miller,et al.  Climate Change 2007: The Physical Science Basis , 2007 .

[4]  Nancy D. Lewis,et al.  Climate Variability and Change and Their Potential Health Effects in Small Island States: Information for Adaptation Planning in the Health Sector , 2006, Environmental health perspectives.

[5]  Bo Bergbäck,et al.  Climate change - An uncertainty factor in risk analysis of contaminated land. , 2011, The Science of the total environment.

[6]  Maja J. Mataric,et al.  Maximizing Reward in a Non-Stationary Mobile Robot Environment , 2003, Autonomous Agents and Multi-Agent Systems.

[7]  Pim Martens,et al.  Methods of assessing human health vulnerability and public health adaptation to climate change , 2003 .

[8]  W. Collins,et al.  Global climate projections , 2007 .

[9]  Konrad J. Swanepoel.  Unit Distances and Diameters in Euclidean Spaces , 2009, Discret. Comput. Geom..

[10]  M. Futter,et al.  Modelling the long term impact of climate change on the carbon budget of Lake Simcoe, Ontario using INCA-C. , 2012, The Science of the total environment.

[11]  Jie Huang,et al.  Rare Event Detection in a Spatiotemporal Environment , 2006, 2006 IEEE International Conference on Granular Computing.

[12]  J. R. Scotti,et al.  Available From , 1973 .

[13]  Eun-Sung Chung,et al.  Development of spatial water resources vulnerability index considering climate change impacts. , 2011, The Science of the total environment.

[14]  Nirvana Meratnia,et al.  Outlier Detection Techniques for Wireless Sensor Networks: A Survey , 2008, IEEE Communications Surveys & Tutorials.

[15]  M. Stuart,et al.  A review of the impact of climate change on future nitrate concentrations in groundwater of the UK. , 2011, The Science of the total environment.

[16]  Simon Wilson,et al.  How does climate change influence Arctic mercury? , 2012, The Science of the total environment.

[17]  CuiMao-chang,et al.  EI NINO PHENOMENON AND EXTENDED ASSOCIATE PATTERN ANALYSIS , 2004 .

[18]  F. Giorgi,et al.  Does the model regional bias affect the projected regional climate change? An analysis of global model projections , 2010 .

[19]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[20]  Jugal K. Kalita,et al.  A Survey of Outlier Detection Methods in Network Anomaly Identification , 2011, Comput. J..

[21]  A J Wade,et al.  Strengthening the link between climate, hydrological and species distribution modeling to assess the impacts of climate change on freshwater biodiversity. , 2012, The Science of the total environment.

[22]  J.R.E. Harger,et al.  Air-temperature variations and ENSO effects in Indonesia, the Philippines and El Salvador. ENSO patterns and changes from 1866–1993 , 1995 .

[23]  Michael Hahsler,et al.  Analyzing taxonomic classification using extensible Markov models , 2010, Bioinform..

[24]  Vasant Honavar,et al.  Semi-supervised prediction of protein subcellular localization using abstraction augmented Markov models , 2010, BMC Bioinformatics.

[25]  Victoria J. Hodge,et al.  A Survey of Outlier Detection Methodologies , 2004, Artificial Intelligence Review.

[26]  M. Roth,et al.  Temporal dynamics of the urban heat island of Singapore , 2006 .

[27]  C. S. Chang,et al.  A feature based frequency domain analysis algorithm for fault detection of induction motors , 2011, 2011 6th IEEE Conference on Industrial Electronics and Applications.