Prediction of Frost Occurrences Using Statistical Modeling Approaches

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.

Prediction of Frost Events Using Machine Learning and IoT Sensing Devices

“…Lee et al [20] use logistic regression and decision trees to estimate the minimal temperature from eight weather variables for each station in South Korea, for frost events between 1973 and 2007, with the following results: average recall values between 78% and 80% and false alarm rate of (in average) between 22% and 28%.…”

Integration of Google Play Content and Frost Prediction Using CNN: Scalable IoT Framework for Big Data

“…Prediction of frost events can also be made using statistical modeling. In South Korea, frost warning systems have been deployed using logistic regression and decision trees [20]. In [21], the author had said that in 2013, in Argentina, the frost events have destroyed a large production of peach orchards.…”

Frost Forecasting considering Geographical Characteristics

“…Lee et al [7] selected the decision tree as a frost prediction model because it has a higher probability of detection than the logic regression analysis with data from six observatories on the Korean Peninsula. In addition, using autoregressive models with external input and MLP models, Castaneda-Miranda and Castano [8] predicted the temperature inside a greenhouse using the external air temperature, ambient air relative humidity, wind speed, global solar radiation flux, and internal air relative humidity variables.…”