{"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}
引用次数: 0
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.