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.

Abstract
 1. 서 론
 2. 재료 및 방법
  2.1. 데이터 마이닝 이론
  2.2. 분석 및 모델 평가 방법
 3. 결과 및 고찰
  3.1. 분석 결과
  3.2. 모델 평가
 4. 결 론
 감사의 글
 REFERENCES

• 이영미((주)에코브레인, Ecobrain Co. Ltd) | Young-Mi Lee Corresponding author
• 배주현((주)에코브레인, Ecobrain Co. Ltd) | Joo-Hyun Bae
• 박다빈((주)에코브레인, Ecobrain Co. Ltd) | Da-Bin Park