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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

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