Prediction of Frost Occurrences Using Statistical Modeling Approaches

Hyojin Lee,Jong A Chun,Hyun-Hee Han,Sung Kim

doi:10.1155/2016/2075186

Hyojin Lee, Jong A Chun + Show 2 more

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https://doi.org/10.1155/2016/2075186

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Abstract

We developed the frost prediction models in spring in Korea using logistic regression and decision tree techniques. Hit Rate (HR), Probability of Detection (POD), and False Alarm Rate (FAR) from both models were calculated and compared. Threshold values for the logistic regression models were selected to maximize HR and POD and minimize FAR for each station, and the split for the decision tree models was stopped when change in entropy was relatively small. Average HR values were 0.92 and 0.91 for logistic regression and decision tree techniques, respectively, average POD values were 0.78 and 0.80 for logistic regression and decision tree techniques, respectively, and average FAR values were 0.22 and 0.28 for logistic regression and decision tree techniques, respectively. The average numbers of selected explanatory variables were 5.7 and 2.3 for logistic regression and decision tree techniques, respectively. Fewer explanatory variables can be more appropriate for operational activities to provide a timely warning for the prevention of the frost damages to agricultural crops. We concluded that the decision tree model can be more useful for the timely warning system. It is recommended that the models should be improved to reflect local topological features.

Highlights

It is widely known that many perennial crops such as fruit tree in South Korea are prone to be damaged from late-spring frost events
We identified the most selected meteorological variables by the two techniques and compared the frost prediction models from both techniques
The Hit Rate (HR), Probability of Detection (POD), and False Alarm Rate (FAR) values resulting from these threshold values were approximately 0.9, 0.8, and 0.2, respectively (Table 4)

Summary

Introduction

It is widely known that many perennial crops such as fruit tree in South Korea are prone to be damaged from late-spring frost events. Kwon et al [4] analyzed the following eight meteorological variables at each station in South Korea when frost events occurred from 1973 to 2007: minimum temperature (denoted as TMIN), grass minimum temperature (denoted as GMINT), dewpoint temperature (denoted as Dewpoint), and wind speed (denoted as Wind) on frost occurrence days, mean relative humidity (denoted as RHmean), minimum relative humidity (denoted as RHmin), and cloud amount (denoted as Cloud) on one day before the frost occurrence days, and difference between maximum temperature on one day before the frost occurrence days and minimum temperature on the frost occurrence days (denoted as Tdiff ) These meteorological variables have been used to estimate the frost probability. Floor [6] used wind speed, total cloud amount, minimum temperature, and grass minimum temperature for the estimation of frost events at Eelde (Netherlands)

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