Database System and Model for Predicting Risk Level of Flood That Damages Rice Farming in Thailand

  • Wittawin Susutti Department of Mathematics, Faculty of Science, King Mongkut’s University of Technology Thonburi
  • Pirun Dilokpatpongsa Department of Mathematics, Faculty of Science, King Mongkut’s University of Technology Thonburi
  • Pawaton Kaemawichanurat King Mongkut's University of Technology Thonburi
  • Teerapol Saleewong Department of Mathematics, Faculty of Science, King Mongkut’s University of Technology Thonburi
  • Boonkong Dhakonlayodhin Department of Applied Statistics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok
  • Thidaporn Supapakorn Department of Statistics, Faculty of Science, Kasetsart University
  • Wiboonsak Watthayu Department of Mathematics, Faculty of Science, King Mongkut’s University of Technology Thonburi
DOI: https://doi.org/10.19139/soic-2310-5070-2595
Keywords: Crop Damage Area Assessment, Farmer Relief Fund, Disaster, Flood

Abstract

Rice is always an important economic crop of Thailand as it is not only the staple in every family of the entire country but it also earns the extremely large income among all the Thai crop exports. However, Thai farmers are considered starve and still have to face many difficulties, in particular, flood problem. The flood problem has destroyed rice farming areas over the past decade until now. Risk and severity assessments mainly contribute the government promptly subsidizing the farmers. In any case, the updated and reliable database systems are the main ingredients to develop the model of these assessments. In this paper, we develop a database system from surveying with 5,000 samples over the whole country. All the raw data has been managed to clean and prepare in order to develop model that is used to predict risk level. The model achieves 87.24 percent accuracy with significant level of 0.05. In addition, the model is able to select variables that have a statistically significant effect on the risk level forecast, and these variables could be used to improve the quality and data structure for developing Web Application (WebApp). The WebApp of our research group for individual risk assessment of the rice farmers has been developed by Javascript to the front end while the back end is run by Python. The WebApp was evaluated satisfactions by over 370 farmers from three public hearings. The satisfaction average scores are over 4 to maximum 5 in all categories.
Published
2025-09-11
How to Cite
Susutti, W., Dilokpatpongsa, P., Kaemawichanurat, P., Saleewong, T., Dhakonlayodhin, B., Supapakorn , T., & Watthayu, W. (2025). Database System and Model for Predicting Risk Level of Flood That Damages Rice Farming in Thailand. Statistics, Optimization & Information Computing. https://doi.org/10.19139/soic-2310-5070-2595
Issue
Online First
Section
Research Articles
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