Modeling Method for Temperature Anomaly Analysis

Kittisak Kerdprasop, Paradee Chuaybamroong, Nittaya Kerdprasop

2018

Abstract

This study applies intelligent analytical methods to analyze temperature anomaly events during the past seven centuries of countries in the Southeast Asia including Thailand, Malaysia, Myanmar, and Cambodia. The temperature reconstruction during the years 1300 to 1999 were used as data source for anomaly analysis. In the analytical process, correlation analysis was applied to initially investigate the temperature variability concordance among the Southeast Asian countries. The results are that temperature variability patterns in Thailand, Myanmar, and Cambodia are moderately correlated to each other. On the contrary, the temperature variation patterns of Malaysia do not correlate to other countries in the same region. The further in-depth analysis focuses on the temperature anomaly of Thailand that shows high variability from the 14th to 16th centuries. Several machine learning algorithms had been applied to estimate the temperature anomaly of Thailand based on the anomaly events among the neighbors. The learned models reveal that Myanmar temperature anomaly most associate to the Thailand’s temperature variation. The performance of each model had been assessed and the results reveal that the chi-squared automatic interaction detection, or CHAID, is the best one with 0.624 correlation coefficient and relative error around 0.611.

Paper Citation

in Harvard Style

Kerdprasop K., Chuaybamroong P. and Kerdprasop N. (2018). Modeling Method for Temperature Anomaly Analysis. In Proceedings of the 10th International Joint Conference on Computational Intelligence (IJCCI 2018) - Volume 1: IJCCI; ISBN 978-989-758-327-8, SciTePress, pages 274-280. DOI: 10.5220/0007224902740280

in Bibtex Style

@conference{ijcci18,
author={Kittisak Kerdprasop and Paradee Chuaybamroong and Nittaya Kerdprasop},
title={Modeling Method for Temperature Anomaly Analysis},
booktitle={Proceedings of the 10th International Joint Conference on Computational Intelligence (IJCCI 2018) - Volume 1: IJCCI},
year={2018},
pages={274-280},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007224902740280},
isbn={978-989-758-327-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Computational Intelligence (IJCCI 2018) - Volume 1: IJCCI
TI - Modeling Method for Temperature Anomaly Analysis
SN - 978-989-758-327-8
AU - Kerdprasop K.
AU - Chuaybamroong P.
AU - Kerdprasop N.
PY - 2018
SP - 274
EP - 280
DO - 10.5220/0007224902740280
PB - SciTePress