Article: Frost forecast - a practice of machine learning from data Journal: International Journal of Reasoning-based Intelligent Systems (IJRIS) 2021 Vol.13 No.4 pp.191 - 203 Abstract: Among the efforts in frost forecast using machine learning techniques, a well-adopted method is to first apply time series forecast for the lowest temperature at future time points, such as the next a few days, and then apply predictive model to predict the event of frost at these time points using corresponding temperature forecasted. According to the domain understanding, there exists some 'cause-effect' between environment factors, including temperature and others, and the occurrence of frost in a few hours' period. A new modelling concept has been proposed by Ding et al. to capture such cause-effect. Preliminary experiments showed encouraging results with a sample of minute-level sensor data collected in Ikuta campus of Meiji University. In this article, as a continuation of the work, we shall further discuss methods of modelling, including causal models and associative models, and propose a framework of hybrid system in supporting frost forecast of short-term (e.g., a few hours) as well as that of relatively longer periods (e.g., a few days). More experiments are provided, and the issues of performance evaluation are discussed. Inderscience Publishers - linking academia, business and industry through research

Title: Frost forecast - a practice of machine learning from data

Authors: Liya Ding; Yosuke Tamura; Kosuke Noborio; Kazuki Shibuya

Addresses: School of Science and Technology, Meiji University, 1-1-1, Higashimita, Tama-ku, Kawasaki, 214-8571, Japan ' School of Science and Technology, Meiji University, 1-1-1, Higashimita, Tama-ku, Kawasaki, 214-8571, Japan ' School of Agriculture, Meiji University, 1-1-1, Higashimita, Tama-ku, Kawasaki, 214-8571, Japan ' School of Agriculture, Meiji University, 1-1-1, Higashimita, Tama-ku, Kawasaki, 214-8571, Japan

Abstract: Among the efforts in frost forecast using machine learning techniques, a well-adopted method is to first apply time series forecast for the lowest temperature at future time points, such as the next a few days, and then apply predictive model to predict the event of frost at these time points using corresponding temperature forecasted. According to the domain understanding, there exists some 'cause-effect' between environment factors, including temperature and others, and the occurrence of frost in a few hours' period. A new modelling concept has been proposed by Ding et al. to capture such cause-effect. Preliminary experiments showed encouraging results with a sample of minute-level sensor data collected in Ikuta campus of Meiji University. In this article, as a continuation of the work, we shall further discuss methods of modelling, including causal models and associative models, and propose a framework of hybrid system in supporting frost forecast of short-term (e.g., a few hours) as well as that of relatively longer periods (e.g., a few days). More experiments are provided, and the issues of performance evaluation are discussed.

Keywords: frost forecast; machine learning; prediction; time series forecasting; cause-effect.

DOI: 10.1504/IJRIS.2021.118642

International Journal of Reasoning-based Intelligent Systems, 2021 Vol.13 No.4, pp.191 - 203

Received: 04 Mar 2020
Accepted: 12 May 2020
Published online: 30 Oct 2021 *

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Title: Frost forecast - a practice of machine learning from data

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