Comparisons of Machine Learning Methods of Statistical Downscaling Method: Case Studies of Daily Climate Anomalies in Thailand

Authors

  • Kanawut Chattrairat Technology of Information System Management, Faculty of Engineering, Mahidol University, Thailand https://orcid.org/0000-0002-6429-9369
  • Waranyu Wongseree Department of Electrical and Computer Engineering, King Mongkut’s University of Technology North Bangkok, Thailand
  • Adisorn Leelasantitham Technology of Information System Management, Faculty of Engineering, Mahidol University, Thailand

DOI:

https://doi.org/10.13052/jwe1540-9589.2057

Keywords:

Global Climate Model (GCM). Statistical downscaling method, Linear Regression (LR), Gaussian Process (GP), Support Vector Machine (SVM) and Deep Learning (DL)

Abstract

The climate change which is essential for daily life and especially agriculture has been forecasted by global climate models (GCMs) in the past few years. Statistical downscaling method (SD) has been used to improve the GCMs and enables the projection of local climate. Many pieces of research have studied climate change in case of individually seasonal temperature and precipitation for simulation; however, regional difference has not been included in the calculation. In this research, four fundamental SDs, linear regression (LR), Gaussian process (GP), support vector machine (SVM) and deep learning (DL), are studied for daily maximum temperature (TMAX), daily minimum temperature (TMIN), and precipitation (PRCP) based on the statistical relationship between the larger-scale climate predictors and predictands in Thailand. Additionally, the data sets of climate variables from over 45 weather stations overall in Thailand are used to calculate in this calculation. The statistical analysis of two performance criteria (correlation and root mean square error (RMSE)) shows that the DL provides the best performance for simulation. The TMAX and TMIN were calculated and gave a similar trend for all models. PRCP results found that in the North and South are adequate and poor performance due to high and low precipitation, respectively. We illustrate that DL is one of the suitable models for the climate change problem.

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Author Biographies

Kanawut Chattrairat, Technology of Information System Management, Faculty of Engineering, Mahidol University, Thailand

Kanawut Chattrairat is a Ph.D. student at IT Management Division, Faculty of Engineering, Mahidol University, Bangkok, Thailand. He received a B.Eng in Computer Engineering and a M.Sci. in Technology of Information Management. His research interests in Machine learning and Data processing. He is a software engineer with extensive experience and management skills and works for Financial Services Technology company. The company provides payment and banking solutions for the Bank around the glove.

Waranyu Wongseree, Department of Electrical and Computer Engineering, King Mongkut’s University of Technology North Bangkok, Thailand

Waranyu Wongseree received the B.E., M.E., and Ph.D. degrees in electrical engineering from the King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand. His research interests include applied machine learning, climate model, bioinformatics, and home energy monitoring.

Adisorn Leelasantitham, Technology of Information System Management, Faculty of Engineering, Mahidol University, Thailand

Adisorn Leelasantitham received the B.Eng. in Electronics and Telecommunications and M. Eng. in Electrical Engineering from King Mongkut’s University of Technology Thonburi (KMUTT), Thailand, in 1997 and 1999, respectively. He received his PhD degree in Electrical Engineering from Sirindhorn International Institute of Technology (SIIT), Thammasat University, Thailand, in 2005. He is currently the Associate Professor in Technology of Information System Management Program, Faculty of Engineering, Mahidol University, Thailand. His research interests include Applications of Blockchain Technology and Cryptocurrency, e.g. electricity trading platform, etc.,conceptual models for IT managements, image processing, AI, neural networks, machine learning, IoT platforms, data analytics, chaos systems and healthcare IT. He is a member of the IEEE.

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Published

2021-07-19

Issue

Section

Communication, Multimedia and Learning Technology through Future Web Engineering