{"title":"数据同化与机器学习:鱼类捕捞预测的比较研究","authors":"Yuka Horiuchi, Yuya Kokaki, Tetsunori Kobayashi, Tetsuji Ogawa","doi":"10.1109/OCEANSE.2019.8867066","DOIUrl":null,"url":null,"abstract":"Data assimilation (DA) and machine learning (ML) are empirically compared for automatic daily fish catch forecasting (DFCF). ML would be a promising approach if large-scale data are available for training. Otherwise, DA would perform well, where prior knowledge on a monitoring target is incorporated into modeling. The present study aims to clarify the robustness of both approaches in DFCF with a small amount of data, and their evolution as the amount of training data increases. Experimental comparisons using catch and meteorological data demonstrate that a DA-based DFCF system yields a significant improvement over an ML-based systems with a small amount of data, and is comparable with ML-based systems with sufficient amount of data.","PeriodicalId":375793,"journal":{"name":"OCEANS 2019 - Marseille","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Data Assimilation Versus Machine Learning: Comparative Study Of Fish Catch Forecasting\",\"authors\":\"Yuka Horiuchi, Yuya Kokaki, Tetsunori Kobayashi, Tetsuji Ogawa\",\"doi\":\"10.1109/OCEANSE.2019.8867066\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Data assimilation (DA) and machine learning (ML) are empirically compared for automatic daily fish catch forecasting (DFCF). ML would be a promising approach if large-scale data are available for training. Otherwise, DA would perform well, where prior knowledge on a monitoring target is incorporated into modeling. The present study aims to clarify the robustness of both approaches in DFCF with a small amount of data, and their evolution as the amount of training data increases. Experimental comparisons using catch and meteorological data demonstrate that a DA-based DFCF system yields a significant improvement over an ML-based systems with a small amount of data, and is comparable with ML-based systems with sufficient amount of data.\",\"PeriodicalId\":375793,\"journal\":{\"name\":\"OCEANS 2019 - Marseille\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"OCEANS 2019 - Marseille\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/OCEANSE.2019.8867066\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"OCEANS 2019 - Marseille","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/OCEANSE.2019.8867066","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Data Assimilation Versus Machine Learning: Comparative Study Of Fish Catch Forecasting
Data assimilation (DA) and machine learning (ML) are empirically compared for automatic daily fish catch forecasting (DFCF). ML would be a promising approach if large-scale data are available for training. Otherwise, DA would perform well, where prior knowledge on a monitoring target is incorporated into modeling. The present study aims to clarify the robustness of both approaches in DFCF with a small amount of data, and their evolution as the amount of training data increases. Experimental comparisons using catch and meteorological data demonstrate that a DA-based DFCF system yields a significant improvement over an ML-based systems with a small amount of data, and is comparable with ML-based systems with sufficient amount of data.