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Rainfall Estimation in Weather Radar Using Support Vector Machine

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Intelligent Information and Database Systems (ACIIDS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9011))

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Abstract

Estimation of rainfall is a very important issue for weather and flood forecasting. However, the traditional rainfall estimation is not precise enough. The traditional rainfall estimation method used the Z-R relation to estimate the rainfall rate. However, when applying the Z-R relation in the real rainfall estimation, there are many limitations. Thus, this paper proposes a method to estimate the rainfall in weather radar and to solve above-mentioned problems. The proposed method first extracts the radar reflectivity and radial velocity in a region which based on the Taipei weather station as the features. And then, these features are trained by support vector machine (SVM) to obtain the rainfall estimation model. Last, this model is used to estimate the rainfall in the weather radar. Experimental results show that the proposed method can estimate the rainfall and achieving approximately 70 % rainfall estimation rates.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Taipei, No. 1, Ai-Kuo W. Road, Taipei, 100, Taiwan, ROC

    Bo-Jhen Huang & Chun-Ming Tsai

  2. Department of Communication Engineering, Oriental Institute of Technology, New Taipei City, 220, Taiwan, ROC

    Teng-Hui Tseng

Authors
  1. Bo-Jhen Huang
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  2. Teng-Hui Tseng
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  3. Chun-Ming Tsai
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Corresponding author

Correspondence to Chun-Ming Tsai .

Editor information

Editors and Affiliations

  1. Ton Duc Thang University, Ho Chi Minh city, Vietnam

    Ngoc Thanh Nguyen

  2. Wroclaw University of Technology, Wroclaw, Poland

    Bogdan Trawiński

  3. Bina Nusantara University, Jakarta, Indonesia

    Raymond Kosala

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© 2015 Springer International Publishing Switzerland

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Huang, BJ., Tseng, TH., Tsai, CM. (2015). Rainfall Estimation in Weather Radar Using Support Vector Machine. In: Nguyen, N., Trawiński, B., Kosala, R. (eds) Intelligent Information and Database Systems. ACIIDS 2015. Lecture Notes in Computer Science(), vol 9011. Springer, Cham. https://doi.org/10.1007/978-3-319-15702-3_56

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  • DOI: https://doi.org/10.1007/978-3-319-15702-3_56

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-15701-6

  • Online ISBN: 978-3-319-15702-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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