数据同化与机器学习:鱼类捕捞预测的比较研究

Yuka Horiuchi, Yuya Kokaki, Tetsunori Kobayashi, Tetsuji Ogawa
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引用次数: 0

摘要

对数据同化(DA)和机器学习(ML)在自动每日渔获量预测(DFCF)中的应用进行了实证比较。如果有大规模数据可用于训练,ML将是一种很有前途的方法。否则,在将监视目标的先验知识纳入建模的情况下,数据分析将表现良好。本研究旨在阐明两种方法在少量数据的DFCF中的鲁棒性,以及它们随着训练数据量的增加而演变。使用捕鱼量和气象数据进行的实验比较表明,基于数据分析的DFCF系统比具有少量数据的基于机器学习的系统产生了显著的改进,并且与具有足够数据量的基于机器学习的系统相当。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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