Xueqian Fu , Chunyu Zhang , Fuhao Chang , Lingling Han , Xiaolong Zhao , Zhengjie Wang , Qiaoyu Ma
{"title":"Simulation and forecasting of fishery weather based on statistical machine learning","authors":"Xueqian Fu , Chunyu Zhang , Fuhao Chang , Lingling Han , Xiaolong Zhao , Zhengjie Wang , Qiaoyu Ma","doi":"10.1016/j.inpa.2023.05.001","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 1","pages":"Pages 127-142"},"PeriodicalIF":7.7000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214317323000537/pdfft?md5=7f82be8c0d8c5961e92ee2d280abc699&pid=1-s2.0-S2214317323000537-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Processing in Agriculture","FirstCategoryId":"1091","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214317323000537","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍：
Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining
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