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Theoretical and Applied Climatology

, Volume 126, Issue 3–4, pp 453–467 | Cite as

Statistical downscaling rainfall using artificial neural network: significantly wetter Bangkok?

  • Minh Tue Vu
  • Thannob Aribarg
  • Siriporn Supratid
  • Srivatsan V Raghavan
  • Shie-Yui Liong
Original Paper

Abstract

Artificial neural network (ANN) is an established technique with a flexible mathematical structure that is capable of identifying complex nonlinear relationships between input and output data. The present study utilizes ANN as a method of statistically downscaling global climate models (GCMs) during the rainy season at meteorological site locations in Bangkok, Thailand. The study illustrates the applications of the feed forward back propagation using large-scale predictor variables derived from both the ERA-Interim reanalyses data and present day/future GCM data. The predictors are first selected over different grid boxes surrounding Bangkok region and then screened by using principal component analysis (PCA) to filter the best correlated predictors for ANN training. The reanalyses downscaled results of the present day climate show good agreement against station precipitation with a correlation coefficient of 0.8 and a Nash-Sutcliffe efficiency of 0.65. The final downscaled results for four GCMs show an increasing trend of precipitation for rainy season over Bangkok by the end of the twenty-first century. The extreme values of precipitation determined using statistical indices show strong increases of wetness. These findings will be useful for policy makers in pondering adaptation measures due to flooding such as whether the current drainage network system is sufficient to meet the changing climate and to plan for a range of related adaptation/mitigation measures.

Keywords

Artificial Neural Network Artificial Neural Network Model Reanalysis Data Southwest Monsoon Statistical Downscaling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors thank the Center for Hazards Research, Department of Civil and Environmental Engineering and CENSAM, SMART, both at the National University of Singapore. The second co-author wishes to thank the Thailand Research Fund through the Royal Golden Jubilee Ph.D. Programme (PHD/0221/2550) funded to Rangsit University for the doctoral programme and the Tropical Marine Science Institute, National University of Singapore, where he spent his internship.

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Copyright information

© Springer-Verlag Wien 2015

Authors and Affiliations

  • Minh Tue Vu
    • 1
  • Thannob Aribarg
    • 2
    • 3
  • Siriporn Supratid
    • 3
  • Srivatsan V Raghavan
    • 1
  • Shie-Yui Liong
    • 1
    • 4
  1. 1.Tropical Marine Science InstituteNational University of SingaporeSingaporeSingapore
  2. 2.Climate Change and Disaster CentreRangsit UniversityRangsitThailand
  3. 3.Department of Information TechnologyRangsit UniversityRangsitThailand
  4. 4.Willis Research Network, Willis Re Inc.LondonUK

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