Applying classification to rainfall nowcasting with topographical awareness

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2018 1st International Cognitive Cities Conference, IC3 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages37-42
Number of pages6
ISBN (Electronic)9781538650592
DOIs
Publication statusPublished - 2018 Dec 6
Event1st International Cognitive Cities Conference, IC3 2018 - Okinawa, Japan
Duration: 2018 Aug 72018 Aug 9

Publication series

NameProceedings - 2018 1st International Cognitive Cities Conference, IC3 2018

Other

Other1st International Cognitive Cities Conference, IC3 2018
CountryJapan
CityOkinawa
Period18-08-0718-08-09

All Science Journal Classification (ASJC) codes

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

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