Database System and Model for Predicting Risk Level of Flood That Damages Rice Farming in Thailand - Wittawin Susutti; Pirun Dilokpatpongsa; Pawaton Kaemawichanurat; Teerapol Saleewong; Boonkong Dhakonlayodhin; Thidaporn Supapakorn; Wiboonsak Watthayu (Statistics, Optimization & Information Computing, 2025)
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.