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http://dx.doi.org/10.3741/JKWRA.2021.54.9.667

Comparative assessment of frost event prediction models using logistic regression, random forest, and LSTM networks  

Chun, Jong Ahn (Prediction Research Department, Climate Services and Research Division, APEC Climate Center)
Lee, Hyun-Ju (Climate Analytics Department, Climate Services and Research Division, APEC Climate Center)
Im, Seul-Hee (Climate Analytics Department, Climate Services and Research Division, APEC Climate Center)
Kim, Daeha (Department of Civil Engineering, Jeonbuk National University)
Baek, Sang-Soo (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Publication Information
Journal of Korea Water Resources Association / v.54, no.9, 2021 , pp. 667-680 More about this Journal
Abstract
We investigated changes in frost days and frost-free periods and to comparatively assess frost event prediction models developed using logistic regression (LR), random forest (RF), and long short-term memory (LSTM) networks. The meteorological variables for the model development were collected from the Suwon, Cheongju, and Gwangju stations for the period of 1973-2019 for spring (March - May) and fall (September - November). The developed models were then evaluated by Precision, Recall, and f-1 score and graphical evaluation methods such as AUC and reliability diagram. The results showed that significant decreases (significance level of 0.01) in the frequencies of frost days were at the three stations in both spring and fall. Overall, the evaluation metrics showed that the performance of RF was highest, while that of LSTM was lowest. Despite higher AUC values (above 0.9) were found at the three stations, reliability diagrams showed inconsistent reliability. A further study is suggested on the improvement of the predictability of both frost events and the first and last frost days by the frost event prediction models and reliability of the models. It would be beneficial to replicate this study at more stations in other regions.
Keywords
Frost; Frost-free period; Logistic regression; Random forest; Long short-term memory (LSTM) network;
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Times Cited By KSCI : 1  (Citation Analysis)
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