Improving current forecast by Leveraging Measured Data and numerical models via LiESNs

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Felipe M. Moreno , Marcel R. de Barros , Artur Jordão , Marlon S. Mathias , Marcelo Dottori , Anna H. Reali Costa , Edson S. Gomi , Fabio G. Cozman , Eduardo A. Tannuri
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引用次数: 0

Abstract

Forecasting metocean conditions is essential for applications such as navigation and maritime operations. In this work, Leaky-integrator Echo State Networks (LiESN) are investigated to combine irregular time series of measurements to numerically modeled currents, producing a forecast of currents for a port entrance channel. The evaluated method can assimilate data from irregular time series, automatically addressing missing data. Different settings that convey scenarios with data unavailability before the forecast are evaluated using the Index of Agreement (IOA) and Mean Absolute Error (MAE) as the performance metrics. Results show that while a numerical model does not improve accuracy, it improves the system’s robustness in the case of missing data.

Abstract Image

通过LiESNs利用测量数据和数值模型改进当前预测
预测海洋气象条件对于导航和海上作业等应用至关重要。在这项工作中,研究了泄漏积分器回声状态网络(LiESN)将不规则时间序列测量与数值模拟电流相结合,从而产生港口入口通道的电流预测。评估后的方法可以吸收不规则时间序列的数据,自动寻址缺失数据。在预测之前,使用协议指数(IOA)和平均绝对误差(MAE)作为性能指标来评估传递数据不可用情景的不同设置。结果表明,虽然数值模型不能提高精度,但它提高了系统在缺失数据情况下的鲁棒性。
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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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