Simulation and forecasting of fishery weather based on statistical machine learning

Xueqian Fu; Chunyu Zhang; Fuhao Chang; Lingling Han; Xiaolong Zhao; Zhengjie Wang; Qiaoyu Ma

Information Processing in Agriculture (Mar 2024)

Simulation and forecasting of fishery weather based on statistical machine learning

Xueqian Fu,
Chunyu Zhang,
Fuhao Chang,
Lingling Han,
Xiaolong Zhao,
Zhengjie Wang,
Qiaoyu Ma

Affiliations

Xueqian Fu: National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China; Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock, Ministry of Agriculture and Rural Affairs, Beijing, China; Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China; College of Information and Electrical Engineering, China Agricultural University, Beijing, China; Corresponding author at: CIEE 633, 17 Qinghua Donglu, Beijing 100083, China.
Chunyu Zhang: National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China; Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock, Ministry of Agriculture and Rural Affairs, Beijing, China; Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China; College of Information and Electrical Engineering, China Agricultural University, Beijing, China
Fuhao Chang: National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China; Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock, Ministry of Agriculture and Rural Affairs, Beijing, China; Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China; College of Information and Electrical Engineering, China Agricultural University, Beijing, China
Lingling Han: National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
Xiaolong Zhao: National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China; Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock, Ministry of Agriculture and Rural Affairs, Beijing, China; Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China; College of Information and Electrical Engineering, China Agricultural University, Beijing, China
Zhengjie Wang: National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
Qiaoyu Ma: National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China; Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock, Ministry of Agriculture and Rural Affairs, Beijing, China; Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China; College of Information and Electrical Engineering, China Agricultural University, Beijing, China

Journal volume & issue: Vol. 11, no. 1
pp. 127 – 142

Abstract

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As the new generation of artificial intelligence (AI) continues to evolve, weather big data and statistical machine learning (SML) technologies complement each other and are deeply integrated to significantly improve the processing and forecasting accuracy of fishery weather. Accurate fishery weather services play a crucial role in fishery production, serving as a great safeguard for economic benefits and personal safety, enabling fishermen to carry out fishery production better, and contributing to the sustainable development of the fishery industry. The objective of this paper is to offer an understanding of the present state of research and development in SML technology for simulating and forecasting fishery weather. Specifically, we analyze the current state of research and technical features of SML in weather and summarize the applications of SML in simulation and forecasting of fishery weather, which mainly include three aspects: fishery weather scenario generation, fishery weather forecasting, and fishery extreme weather warning. We also illustrate the main technical means and principles of SML technology. Finally, we summarize the most advanced SML fields and provide an outlook on their application value in the field of fishery weather.

Published in Information Processing in Agriculture

ISSN: 2214-3173 (Online)
Publisher: Elsevier
Country of publisher: Netherlands
LCC subjects: Agriculture: Agriculture (General); Technology: Technology (General): Industrial engineering. Management engineering: Information technology
Website: https://www.sciencedirect.com/journal/information-processing-in-agriculture

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