|dc.description.abstract||The purpose of this research project is to develop a model that is able to accurately
predict frost depth on a particular date, using available information. Frost depth prediction
is useful in many applications in several domains. For example in agriculture, knowing
frost depth early is crucial for farmers to determine when and how deep they should plant.
In this study, data is collected primarily from NDAWN(North Dakota AgriculturalWeather
Network) Fargo station for historical soil depth temperature and weather information.
Lasso regression is used to model the frost depth. Since soil temperature is clearly
seasonal, meaning there should be an obvious correlation between temperature and
different days, our model can handle residual correlations that are generated not only
from time domain, but space domain, since temperatures of different levels should also be
correlated. Furthermore, root mean square error (RMSE) is used to evaluate goodness-of-fit
of the model.||en_US