Rainfall Data Fitting based on An Improved Mixture Cosine Model with Markov Chain-Reference-Cited by-同舟云学术

Rainfall Data Fitting based on An Improved Mixture Cosine Model with Markov Chain

Published:2023-01-26 Issue: Volume:20 Page:28-33
ISSN:2224-3402
Container-title:WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS
language:en
Short-container-title:

Author:

Kanchai Thitipong¹,Tepkasetkul Nahatai¹,Pongsart Tippatai²,Klongdee Watcharin¹

Affiliation:

1. Department of Mathematics, Faculty of Science, Khon Kaen University, THAILAND

2. Department of Statistics, Faculty of Science, Khon Kaen University, THAILAND

Abstract

This article proposes a model that uses the adjusted mixture cosine model of two components with Markov chain (MC2MC) for predicting the monthly rainfall with actual data from Khon Kaen meteorological station (381201) in Khon Kaen province, Thailand. The data considers 31 years of historical data from January 1991 to December 2021. The evaluation is measured by the root mean square error (𝑅𝑀𝑆𝐸) and the 𝑅 2 values. We found that the mixture cosine model has 𝑅𝑀𝑆𝐸 and 𝑅 2 values of 70.72 and 52.49%, respectively, and the MC2MC model has 𝑅𝑀𝑆𝐸 and 𝑅 2 values of 42.43 and 82.53%, respectively.

Publisher

World Scientific and Engineering Academy and Society (WSEAS)

Subject

Computer Science Applications,Information Systems

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