A STUDY ON PREDICTION AND APPLICATION OF TRANSPORTATION ROUTES FOR RAIN DISASTERS USING OPEN DATA AND GBDT

• Published in: Artificial Intelligence and Data Science Vol. 4; no. 2; pp. 135 - 141
• Main Authors: MIYATA, Shuta, MIZUNO, Yusuke, TATSUTA, Hitoshi, MORITA, Taisei
• Format: Journal Article
• Language: Japanese
• Published: Japan Society of Civil Engineers 2023; 公益社団法人 土木学会
• Subjects: AI; Disaster Management; GBDT; GIS; Rain Disaster; SHAP
• ISSN: 2435-9262
• DOI: 10.11532/jsceiii.4.2_135

In recent years, there has been a growing need to strengthen disaster prevention and mitigation measures for road facilities in order to prepare for increasingly severe heavy rain disasters. It is important to ensure the effective functioning of the extensive road network in the event of a disaster.In this study, we used GBDT to reproduce a GIS-based search for the shortest route for goods transportation based on the subject of a rain disaster caused by a linear rainfall belt that occurred in August 2022 mainly in the Okitama region of southern Yamagata Prefecture, and improved the prediction accuracy of goods transportation routes when rainfall conditions change by examining explanatory variables using SHAP. In addition, the applicability of the GBDT learning model to future rain disasters was verified by using the model to predict past heavy rain disasters (July 2020). As a result, it was confirmed that the model was able to predict the transportation routes of goods in the past with an accuracy 0.88 (F-value 0.80).