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Short-term Prediction of Localized Heavy Rain from Radar Imaging and Machine Learning
Swe Swe Aung,Yu Senaha,Shin Ohsawa,Itaru Nagayama,Shiro Tamaki 대한전자공학회 2018 IEIE Transactions on Smart Processing & Computing Vol.7 No.2
Heavy rainfall has frequently caused serious flooding and landslides, increasing traffic delays in most parts of the world. Consequently, the people in areas battered by heavy rainfall face many hardships. Thus, the negative effects of torrential rainfall always remind researchers to keep seeking the ways to prevent such damage. Therefore, we designed a system for short-term prediction of localized heavy downpours by using radar images coupled with a machine learning method. Here, we introduce a new approach, named dual k-nearest neighbor (dual-kNN), for shortterm rainfall prediction by upgrading the ordinary classification routines of classical k-nearest neighbors (k-NN). dual-kNN is able to maintain highly robust classification of various K values with an advanced simple dual consideration, where observation of a targeted object can be found not only in the specified region but also in other related regions. We conducted experimentations using 2011, 2013, and 2014 data sets collected from the WITH small-dish aviation radar installed on the rooftop of Information Engineering, University of the Ryukyus. Then, we compared the prediction accuracy of our new approach with classical k-NN. It was experimentally confirmed with test cases and simulations that the performance of dual-kNN is more effective than classical k-NN.
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