[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120676-en":3,"doc-seo-120676-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120676,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","An improved machine learning model Shapley value-based to forecast demand for aquatic product supply chain","An improved machine learning framework is developed to forecast demand in aquatic product cold-chain logistics, addressing limitations in prior models that deliver insufficient predictive performance. Key impact indicators are selected using gray correlation degree to form an indicator system. Time-series, linear regression, and nonlinear approaches are integrated through gray prediction, principal component regression, and BP neural networks, producing a combined forecasting model whose error analysis confirms higher accuracy. Trend extrapolation with time-series modeling estimates influencing factors and CCLD-AP values from 2023 to 2027, supporting planning for ports and surrounding cities.","TYPE Original Research PUBLISHED 30 March 2023  \nDOI 10.3389/fevo.2023.1160684  \nOPEN ACCESS  \nEDITED BY  \nXin Ning,  \nInstitute of Semiconductors (CAS), China  \nREVIEWED BY  \nJafar A. Alzubi,  \nAl-Balqa Applied University, Jordan  \nTamanna Singhdeo, Fairleigh Dickinson University, Canada  \nRamani Selvanambi, VIT University, India  \n*CORRESPONDENCE  \nShanshan Huang  \n [shan0202@pukyong.ac.kr](shan0202@pukyong.ac.kr)  \nSPECIALTY SECTION  \nThis article was submitted to Environmental Informatics and Remote Sensing,  \na section of the journal  \nFrontiers in Ecology and Evolution  \nRECEIVED 07 February 2023  \nACCEPTED 06 March 2023  \nPUBLISHED 30 March 2023  \nCITATION  \nSu X and Huang S (2023) An improved machine learning model Shapley value-based to forecast demand for aquatic product supply chain.  \nFront. Ecol. Evol. 11:1160684 .  \ndoi: 10.3389/fevo.2023.1160684  \nCOPYRIGHT  \n© 2023 Su and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nAn improved machine learning model Shapley value-based to forecast demand for aquatic product supply chain  \nXin Su 1 and Shanshan Huang 2*  \n1School of Finance and Accounting, Henan University of Animal Husbandry and Economy, Zhengzhou, Henan, China, 2 Fisheries Policy Implementation Department, Korea Maritime Institute, Busan, Republic of Korea  \nPrevious machine learning models usually faced the problem of poor performance, especially for aquatic product supply chains. In this study, we proposed a coupling machine learning model Shapely value-based to predict the CCL demand of aquatic products (CCLD-AP) . We first select the key impact indicators through the gray correlation degree and finally determine the indicator system. Secondly, gray prediction, principal component regression analysis prediction, and BP neural network models are constructed from the perspective of time series, linear regression and nonlinear, combined with three single forecasts, a combined forecasting model is constructed, the error analysis of all prediction model results shows that the combined prediction results are more accurate. Finally, the trend extrapolation method and time series are combined to predict the independent variable influencing factor value and the CCLD-AP from 2023 to 2027. Our study can provide a reference for the progress of CCLD-AP in ports and their hinterland cities.  \nKEYWORDS  \nmachine learning model, Shapely value, trend extrapolation, aquatic product, single forecast  \n1. Introduction  \nIn modern society, people’s concept of consumption has changed greatly. Due to the need for bodily nutrients, aquatic products have become indispensable foodstuffs in daily life (Kim et al., 2016). According to the principle of supply and demand balance, the output of aquatic products must be increased and the quality of the cold chain must be improved, so the requirements of cold chain logistics (CCL) service also needs to be improved (Abimannanet al., 2023) . China’s cold chain infrastructure is not perfect, the per capita capacity of cold storage is also small, and the number of cold storage enterprises is unevenly distributed. Even in regions with a more developed logistics industry, the cold chain transport process is prone to a “broken chain” phenomenon, which leads to spoilage, leading to a decline in circulation rate. Therefore, in order to balance the supply, a more comprehensive understanding of the aquatic CCL system and accurate prediction of the demand side of the CCL can ensure that the national aquatic products maintain the supply (Abada and Vijay, 2005; Liu and Yang, 2018) .  \nMany pieces","cbCaitNsDdNYeU8B","https://ap.wps.com/l/cbCaitNsDdNYeU8B","pdf",2637969,1,12,"English","en",105,"# Introduction\n## Consumption and aquatic product demand\n## Cold chain logistics constraints\n## Related studies and forecasting methods","[{\"question\":\"What problem does the study target in aquatic product cold-chain forecasting?\",\"answer\":\"It targets the weak performance of existing machine learning models for aquatic product supply chains and cold-chain logistics demand prediction.\"},{\"question\":\"How are key indicators selected for the prediction system?\",\"answer\":\"Key impact indicators are selected using the gray correlation degree, and an indicator system is then established based on the selected indicators.\"},{\"question\":\"Which forecasting models are combined in the proposed approach?\",\"answer\":\"The method combines gray prediction, principal component regression analysis prediction, and a BP neural network model, integrating three single forecasts into a combined model.\"}]","An improved machine learning model Shapley value-based to forecast demand for aquatic product supply chain | 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