Applying classification to rainfall nowcasting with topographical awareness

Yi Jhong Gong, Kai Hsiang Lin, Jui Hung Chang, Ren Hung Hwang

研究成果: Conference contribution

摘要

Rainfall nowcasting provides the estimations of rainfall condition, such as accumulated precipitation, probability of precipitation forecast, and rainfall intensity prediction. Although numerical weather prediction (NWP) can simulate the atmospheric conditions, limited by the computation performance and the initial field data, the NWP does not perform well in short-term forecasting. Since atmosphere environment is a complex non-linear system, we used the deep learning approach to learn and perform the rainfall nowcasting. In this paper, we used the classification model based on a residual network and added the "side path" to input the additional data which could assist our model in acquiring prior knowledge. For the experiment, we input the topographic data to help the model include topographical awareness. In our experiment, the model trained by the additional topographic data achieved the higher accuracy than the model lacking the topographical recognition.

原文English
主出版物標題Proceedings - 2018 1st International Cognitive Cities Conference, IC3 2018
發行者Institute of Electrical and Electronics Engineers Inc.
頁面37-42
頁數6
ISBN(電子)9781538650592
DOIs
出版狀態Published - 2018 十二月 6
事件1st International Cognitive Cities Conference, IC3 2018 - Okinawa, Japan
持續時間: 2018 八月 72018 八月 9

出版系列

名字Proceedings - 2018 1st International Cognitive Cities Conference, IC3 2018

Other

Other1st International Cognitive Cities Conference, IC3 2018
國家Japan
城市Okinawa
期間18-08-0718-08-09

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Education
  • Urban Studies

指紋 深入研究「Applying classification to rainfall nowcasting with topographical awareness」主題。共同形成了獨特的指紋。

引用此