An Attempt to Forecast All Different Rainfall Series by Dynamic Programming Approach

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 880)


Unexpected heavy rainfall has been seriously occurred in most parts of the world, especially during monsoon season. As a serious consequence of heavy rainfall, the people in those areas battered by heavy rainfall faced many hardship lives. Without exception, prevention is the best way of minimizing these negative effects. In spite of all, we developed a rainfall series prediction system for different series patterns by applying the dynamic programming approach aiming to acquire the rainfall level of the whole rainfall cycle. The simple idea behind the proposed dynamic programming approach is to find the similarity of two rainfall sequences upon the maximum match of the rainfall level of those sequences. Based on 2011 and 2013 real data sets collected from WITH radar, which is installed on the rooftop of Information Engineering, University of the Ryukyus, the comparison between the conventional approach (Polynomial Regression) and the proposed approach is investigated. These correlation experiments confirm that the dynamic programming approach is more efficient for predicting different rainfall series.


Dynamic programming Rainfall series Polynomial regression WITH radar 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of SoftwareUniversity of Computer StudiesTaunggyiMyanmar
  2. 2.Weathernews Inc.OkinawaJapan
  3. 3.Department of Information EngineeringUniversity of the RyukyusOkinawaJapan

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