A study on frost prediction model using machine learning
- Authors
- Kim,Hyojeoung; Kim, Sahm
- Issue Date
- Aug-2022
- Publisher
- 한국통계학회
- Keywords
- frost prediction; machine learning; XGB; SVM; Random Forest; MLP
- Citation
- 응용통계연구, v.35, no.4, pp 543 - 552
- Pages
- 10
- Journal Title
- 응용통계연구
- Volume
- 35
- Number
- 4
- Start Page
- 543
- End Page
- 552
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/60848
- DOI
- 10.5351/KJAS.2022.35.4.543
- ISSN
- 1225-066X
2383-5818
- Abstract
- When frost occurs, crops are directly damaged. When crops come into contact with low temperatures, tissues freeze, which hardens and destroys the cell membranes or chloroplasts, or dry cells to death. In July 2020, a sudden sub-zero weather and frost hit the Minas Gerais state of Brazil, the world's largest coffee producer, damaging about 30% of local coffee trees. As a result, coffee prices have risen significantly due to the damage, and farmers with severe damage can produce coffee only after three years for crops to recover, which is expected to cause long-term damage.
In this paper, we tried to predict frost using frost generation data and weather observation data provided by the Korea Meteorological Administration to prevent severe frost. A model was constructed by reflecting weather factors such as wind speed, temperature, humidity, precipitation, and cloudiness. Using XGB(eXtreme Gradient Boosting), SVM(Support Vector Machine), Random Forest, and MLP(Multi Layer perceptron) models, various hyper parameters were applied as training data to select the best model for each model. Finally, the results were evaluated as accuracy(acc) and CSI(Critical Success Index) in test data.
XGB was the best model compared to other models with 90.4% ac and 64.4% CSI, followed by SVM with 89.7% ac and 61.2% CSI. Random Forest and MLP showed similar performance with about 89% ac and about 60% CSI.
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Collections - College of Business & Economics > Department of Applied Statistics > 1. Journal Articles
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