Frost Depth Prediction
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 f...
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ndltd-ndsu.edu-oai-library.ndsu.edu-10365-274882021-10-02T17:09:20Z Frost Depth Prediction Luo, Meng 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. 2018-02-07T22:29:54Z 2018-02-07T22:29:54Z 2014 text/thesis https://hdl.handle.net/10365/27488 NDSU Policy 190.6.2 https://www.ndsu.edu/fileadmin/policy/190.pdf application/pdf North Dakota State University |
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description |
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. |
author |
Luo, Meng |
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Luo, Meng Frost Depth Prediction |
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Luo, Meng |
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Luo, Meng |
title |
Frost Depth Prediction |
title_short |
Frost Depth Prediction |
title_full |
Frost Depth Prediction |
title_fullStr |
Frost Depth Prediction |
title_full_unstemmed |
Frost Depth Prediction |
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frost depth prediction |
publisher |
North Dakota State University |
publishDate |
2018 |
url |
https://hdl.handle.net/10365/27488 |
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AT luomeng frostdepthprediction |
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1719486901063254016 |