Smart city disaster prevention platform information integration displays and practical application in New Taipei City Taiwan

Abstract

Urban areas are gradually being affected by climate change. It is difficult to avoid urban flooding caused by heavy rainfall. Especially road flooding occurs 2-3 times a year in urban areas in the summer of Taiwan, when the regional weather is convective rainfall strong, it is difficult for general weather forecasting models to predict the amount of rainfall in the city in a short period of time. Rainfall areas in urban areas are prone to road flooding. Therefore, the intensity management value (>50dBz) of the radar reflectivity around the city is used to estimate the rainfall and urban flood warning, and the IoT water level monitoring instrument can monitor the water level in the urban rainwater sewer and set the urban flood warning based on the management value. The local low-lying areas of the city can also use CCTV images to identify flooding situation as a tool through AI's CCN deep learning technology and CCTV's flooding big data database that according to CNN's learning, training, and testing, after the completion, CCTV inspection and flood image recognition can be used for urban disaster prevention and relief. Finally, the monitoring data related to urban flooding is collected and displayed through the urban smart flood prevention platform, which provides efficient data collection, increases the response time for disaster relief, and quickly eliminates road flooding in the city. This study takes the urban smart flood prevention platform in New Taipei City, Taiwan as an example.