@inbook{2e760448a0614ca58496911cb7b89fd9,
title = "A Study of Causal Modeling with Time Delay for Frost Forecast Using Machine Learning from Data",
abstract = "Causal modeling with time delay has been proposed as a method for predicting frost occurrence in a short period of time. In this method, environment factors are considered as cause, and used as input variables for prediction of frost. For coping with the uncertainty of prediction rooted in randomness of environment, a granulation of environment factors offers potential. In this study, we show that the accuracy of predicting frost occurrence can be improved by appropriately granulating each of the input environment factors involved.",
author = "Shugo Yoshida and Yosuke Tamura and Kenta Owada and Liya Ding and Kosuke Noborio and Kazuki Shibuya",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.",
year = "2023",
doi = "10.1007/978-3-031-08580-2_24",
language = "English",
series = "Studies in Computational Intelligence",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "265--276",
booktitle = "Studies in Computational Intelligence",
}