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http://dx.doi.org/10.5532/KJAFM.2021.23.4.295

Study on Improvement of Frost Occurrence Prediction Accuracy  

Kim, Yongseok (Climate change Assessment Division, National Institute of Agricultural Sciences)
Choi, Wonjun (Climate change Assessment Division, National Institute of Agricultural Sciences)
Shim, Kyo-moon (Climate change Assessment Division, National Institute of Agricultural Sciences)
Hur, Jina (Climate change Assessment Division, National Institute of Agricultural Sciences)
Kang, Mingu (Climate change Assessment Division, National Institute of Agricultural Sciences)
Jo, Sera (Climate change Assessment Division, National Institute of Agricultural Sciences)
Publication Information
Korean Journal of Agricultural and Forest Meteorology / v.23, no.4, 2021 , pp. 295-305 More about this Journal
Abstract
In this study, we constructed using Random Forest(RF) by selecting the meteorological factors related to the occurrence of frost. As a result, when constructing a classification model for frost occurrence, even if the amount of data set is large, the imbalance in the data set for development of model has been analyzed to have a bad effect on the predictive power of the model. It was found that building a single integrated model by grouping meteorological factors related to frost occurrence by region is more efficient than building each model reflecting high-importance meteorological factors. Based on our results, it is expected that a high-accuracy frost occurrence prediction model will be able to be constructed as further studies meteorological factors for frost prediction.
Keywords
Frost; Random forest; Meteorological factors; Model;
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