Development of statistical and machine learning models to predict the occurrence of radiation fog in Japan

Abstract

AbstractThis study develops statistical and machine learning models based on discriminant analysis, logistic regression analysis, and a support vector machine (SVM) to predict the occurrence of radiation fog in Japan. The selection of a suitable set of explanatory variables for the models was made using the Akaike information criterion (AIC). The accuracies of the three models were measured and compared. To determine the optimum combination of explanatory variables, temperature, humidity, wind speed, precipitation, sunshine, and visibility data were considered. Based on the root mean square error (RMSE) and AIC values, the best combination of variables was found to include: the presence of precipitation, mean wind speed during the night, minimum temperature during the night, the amount of temperature cooling during the night, the minimum humidity during the previous day, and visibility at 18:00. A comparison of the predictive accuracies of the three models using the selected variable combination showed that the discriminant model produced a critical success index (CSI, or threat score) of 22.5, while the logistic regression model and the SVM model both produced better CSI results—with scores of 33.8 and 38.2, respectively.