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A Study on Fog Forecasting Method through Data Mining Techniques in Jeju

데이터마이닝 기법들을 통한 제주 안개 예측 방안 연구

  • Received : 2016.03.21
  • Accepted : 2016.04.04
  • Published : 2016.04.30

Abstract

Fog may have a significant impact on road conditions. In an attempt to improve fog predictability in Jeju, we conducted machine learning with various data mining techniques such as tree models, conditional inference tree, random forest, multinomial logistic regression, neural network and support vector machine. To validate machine learning models, the results from the simulation was compared with the fog data observed over Jeju(184 ASOS site) and Gosan(185 ASOS site). Predictive rates proposed by six data mining methods are all above 92% at two regions. Additionally, we validated the performance of machine learning models with WRF (weather research and forecasting) model meteorological outputs. We found that it is still not good enough for operational fog forecast. According to the model assesment by metrics from confusion matrix, it can be seen that the fog prediction using neural network is the most effective method.

Keywords

Fog prediction;Data mining;R;Tree models;Conditional inference tree;Random forest;Multinomial logistic regression;Neural network;Support vector machine;Confusion matrix

Acknowledgement

Supported by : 산업통상자원부

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