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Research Progress on Time Series Prediction of Agricultural Disaster Risk: A Mini-review
Review Article | Published: 19 November 2024
Agricultural Science and Food Processing Volume 2, Issue 1, 2025: 1-11
Volume 2, Issue 1, Agricultural Science and Food Processing
Volume 2, Issue 1, 2025
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Bin Guo
Bin Guo
Northwest University, China
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Agricultural Science and Food Processing, Volume 2, Issue 1, 2025: 1-11

Open Access | Review Article | 19 November 2024
Research Progress on Time Series Prediction of Agricultural Disaster Risk: A Mini-review
by
Feng Fang Feng Fang 1
Jingjing Lin Jingjing Lin 1 *
Jing Wang Jing Wang 2 *
Peixuan Chen Peixuan Chen 1
Pengcheng Huang Pengcheng Huang 1
Xing Wang Xing Wang 1
Yulong Ma Yulong Ma 1
Liwei Liu Liwei Liu 1
Dawei Wang Dawei Wang 3
1 Lanzhou Regional Climate Center, Lanzhou 730020, China
2 Lanzhou Institute of Arid Meteorology, Lanzhou 730020,China
3 College of Earth Science and Engineering, West Yunnan University of Applied Sciences, Dali 671006, China
* Corresponding Authors: Jingjing Lin, linjj2021@lzu.edu.cn ; Jing Wang, wangjing1102@126.com
DOI: 10.62762/ASFP.2024.610643
Received: 16 October 2024, Accepted: 09 November 2024, Published: 19 November 2024  
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Abstract
Food security is crucial for human survival and national economic development, but frequent meteorological disasters have caused great harm to agricultural production. Therefore, it is very important and meaningful to study how to quickly and accurately predict the loss rate of disasters. Only based on historical loss sequence, the time series prediction method can effectively predict future loss. Therefore, this paper first briefly describes the main means of time series prediction, namely statistical methods and machine learning algorithms. Secondly, the commonly used machine learning algorithms for disaster loss time series prediction, and its application cases and existing problems, were introduced in detail. To address the issue of small sample sizes for loss predication, data augmentation techniques can be used; To address the issue of data non-stationarity, Empirical Mode Decomposition (EMD) can be used to decompose the original sequence into relatively stationary sub-sequences. In addition, exploratory solutions have been proposed, such as ensemble learning strategies for multiple machine learners, and combining machine learning algorithms with optimization algorithms, strong prediction strategies, or attention mechanisms. Finally, a summary solution for conventional disaster damage prediction was proposed.
Keywords
food security
meteorological disasters
time series prediction
machine learning
Data Availability Statement
Not applicable.
Funding
This work was supported without any funding.
Conflicts of Interest
Feng Fang, Jingjing Lin, Peixuan Chen, Pengcheng Huang, Xing Wang, Yulong Ma, Liwei Liu, and Xiaowei Wang are employees of the Lanzhou Regional Climate Center, Lanzhou 730020, China. Jing Wang is an employee of the Lanzhou Institute of Arid Meteorology, Lanzhou 730020, China.
Ethical Approval and Consent to Participate
Not applicable.
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Cite This Article
APA Style
Fang, F., Lin, J., Wang, J., Chen, P., Huang, P., Wang, X., Ma, Y., Liu, L., Wang, D., & Wang, X. (2024). Research Progress on Time Series Prediction of Agricultural Disaster Risk: A Mini-review. Agricultural Science and Food Processing, 2(1), 1–11. https://doi.org/10.62762/ASFP.2024.610643
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