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ISSN:
1798-2340 (Online)
Frequency:
Monthly
DOI:
10.12720/jait
Indexing:
ESCI (Web of Science)
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CNKI
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etc
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Acceptance Rate:
19%
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500 USD
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Impact Factor 2022: 1.0
3.1
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Editor-in-Chief
Prof. Kin C. Yow
University of Regina, Saskatchewan, Canada
I'm delighted to serve as the Editor-in-Chief of
Journal of Advances in Information Technology
.
JAIT
is intended to reflect new directions of research and report latest advances in information technology. I will do my best to increase the prestige of the journal.
What's New
2024-03-28
Vol. 15, No. 3 has been published online!
2024-02-26
The papers published in Vol. 15, Nos. 1&2 have been registered with Crossref.
2024-02-26
Vol. 15, No. 2 has been published online!
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2020
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Volume 11, No. 3, August 2020
>
Annual Rainfall Model by Using Machine Learning Techniques for Agricultural Adjustment
Dulyawit Prangchumpol and Pijitra Jomsri
Department of Information Technology, Faculty of Science and Technology, Suan Sunandha Rajabhat University, Bangkok, Thailand
Abstract
—The change of weather conditions is considered as the major problem which is delicate for populations particularly the developing countries such as Thailand, e.g., the area in the southern region of Thailand, a case study of Andaman seaside which is the area most affected by the change of weather conditions compared with others. This research purposes to develop the model for rainfall forecasting for agricultural adjustment in the areas located at Andaman seaside using Machine Learning Technique to monitor the impact of the change of weather conditions such as the volume of rainfall in each year affecting to agricultural sector by studying the 30-year regressive data in order to estimate the coefficient of the model. The analysis results indicated that the rainfall volume of the areas located at the Andaman seaside of southern region tended to decrease which was resulted from the change of weather conditions where the model was able to provide the awareness of the impact caused by the change of weather to the agriculturists to prepare the proper supporting agricultural plans.
Index Terms
—annual rainfall, machine learning, agricultural
Cite: Dulyawit Prangchumpol and Pijitra Jomsri, "Annual Rainfall Model by Using Machine Learning Techniques for Agricultural Adjustment," Journal of Advances in Information Technology, Vol. 11, No. 3, pp. 161-165, August 2020. doi: 10.12720/jait.11.3.161-165
Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License (
CC BY-NC-ND 4.0
), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.
8-EE038_Thailand
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