Earticle

초록

영어

As climate change accelerates, extreme weather events are becoming more frequent, leading to a growing number of disasters. Taiwan, located in the subtropics, experiences highly diverse and variable weather patterns. Typhoons and torrential rainfall often trigger disasters of different scales, including floods, droughts, and landslides, as well as compound disasters caused by cascading effects. Examples include the nationwide drought in Taiwan in 2020 and the catastrophic Xiaolin Village landslide triggered by Typhoon Morakot. In addition to meteorological hazards, earthquakes are also a major cause of disasters in Taiwan. In disaster situations, an open online platform that connects databases and websites to provide real-time information to the public can greatly accelerate information dissemination and access to historical records. However, disaster information platforms in Taiwan are currently fragmented across different hazard types, and there is no real-time information on disaster-preparedness supplies, resulting in imbalances in the quantity and types of available resources. Therefore, this study aims to establish an interactive geographic information platform for real-time meteorological and multi-hazard disaster intelligence in Taiwan. Built on the Django framework, the platform integrates data from the Central Weather Administration (CWA) and combines meteorological hazard indicators, such as the Standardized Precipitation Index (SPI), with disaster susceptibility data. It further develops visualization maps and databases for various hazards, especially floods and droughts, including real-time disaster-preparedness supplies, rainfall distribution maps, and hazard susceptibility distributions. By linking government management functions with public access and use, this platform can support pre-disaster preparedness, in-disaster allocation and dispatch of emergency resources, and post-disaster loss assessment and statistics. As a tool for data sharing and resource preparedness, it is expected to enhance disaster prevention effectiveness and improve emergency response efficiency. In the future, additional cross-disciplinary hazard susceptibility data can be incorporated to provide more comprehensive disaster information.

저자

• Hsin-Wen Pai [ Department of Civil Engineering, National Central University ]
• Yuan-Chien Lin [ Department of Civil Engineering, National Central University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국재난정보학회 학술발표대회

• 간기

  부정기

• 수록기간

  2005~2026

• 십진분류

  KDC 338 DDC 361