Precipitation prediction in Thailand by using regression analysis
6
Issued Date
2013
Copyright Date
2013
Resource Type
Language
eng
File Type
application/pdf
No. of Pages/File Size
x, 39 leaves : ill.
Access Rights
open access
Rights
ผลงานนี้เป็นลิขสิทธิ์ของมหาวิทยาลัยมหิดล ขอสงวนไว้สำหรับเพื่อการศึกษาเท่านั้น ต้องอ้างอิงแหล่งที่มา ห้ามดัดแปลงเนื้อหา และห้ามนำไปใช้เพื่อการค้า
Rights Holder(s)
Mahidol University
Bibliographic Citation
Thematic Paper (M.Sc. (Technology of Information System Management))--Mahidol University, 2013
Suggested Citation
Yuranan Jaiyeakyen Precipitation prediction in Thailand by using regression analysis. Thematic Paper (M.Sc. (Technology of Information System Management))--Mahidol University, 2013. Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/95212
Title
Precipitation prediction in Thailand by using regression analysis
Alternative Title(s)
การทำนายหยาดน้ำฟ้าในประเทศไทยด้วยวิธีการวิเคราะห์สมการถดถอย
Author(s)
Advisor(s)
Abstract
A methodology is presented for statistical downscaling the general circulation model (GCM) of GFDL-R30 and HadGEM output to predict precipitation at meteorology stations in Thailand under expected future climatic conditions. The meteorology stations that were sampled for testing for prediction were Chiang Mai, Udon Thani, Bangkok, Ubon Ratchathama, Nakhon Ratchasima and Phuket, provinces from each region in Thailand. The approach involves a combination of data preprocessing, data classification and regression analysis. In the preprocessing step, this research used data with GFDL-R30 which was compared between logarithm transformations, square root transformations and no transformations. The results showed the correlation yield of both algorithms was less than no transformations but they can help for negative numbers in predicted data. In the classification step, it applied to the model for cutoff on days that had less rainfall and heavy rainfall. It was varied from 0 mm to 1.5 mm. The results showed the classification cannot increase strong correlation performance. The last section presents about post-processing comparison between multiple linear regressions and Gaussian process which is a non-linear regression. The result showed Gaussian process generally yielded better results both for correlation and RMSE for every station that was tested. Therefore, the Gaussian process is a suitable approach for predicting precipitation in the future without any preprocessing.
Description
Technology of Information System Management (Mahidol University 2013)
Degree Name
Master of Science
Degree Level
Master's degree
Degree Department
Faculty of Engineering
Degree Discipline
Technology of Information System Management
Degree Grantor(s)
Mahidol University