  One of the common hazards and issues in meteorology and agriculture is the
problem of frost, chilling or freezing. This event occurs when the minimum
ambient temperature falls below a certain value. This phenomenon causes a lot
of damage to the country, especially Fars province. Solving this problem
requires that, in addition to predicting the minimum temperature, we can
provide enough time to implement the necessary measures. Empirical methods have
been provided by the Food and Agriculture Organization (FAO), which can predict
the minimum temperature, but not in time. In addition to this, we can use
machine learning methods to model the minimum temperature. In this study, we
have used three methods Gated Recurrent Unit (GRU), Temporal Convolutional
Network (TCN) as deep learning methods, and Gradient Boosting (XGBoost). A
customized loss function designed for methods based on deep learning, which can
be effective in reducing prediction errors. With methods based on deep learning
models, not only do we observe a reduction in RMSE error compared to empirical
methods but also have more time to predict minimum temperature. Thus, we can
model the minimum temperature for the next 24 hours by having the current 24
hours. With the gradient boosting model (XGBoost) we can keep the prediction
time as deep learning and RMSE error reduced. Finally, we experimentally
concluded that machine learning methods work better than empirical methods and
XGBoost model can have better performance in this problem among other
implemented.
