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A Dual Filter Based on Radial Basis Function Neural Networks and Kalman Filters with Application to Numerical Wave Prediction Models

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Publicado en:Sensors vol. 24, no. 24 (2024), p. 8006
Autor principal: Donas, Athanasios
Otros Autores: Kordatos, Ioannis, Alexandridis, Alex, Galanis, George, Famelis, Ioannis Th
Publicado:
MDPI AG
Materias:
Machine learning
Accuracy
Weather forecasting
Datasets
Algorithms
Kalman filters
Neural networks
Data assimilation
Case studies
Bias
Acceso en línea:Citation/Abstract
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Resumen:The aim of this study is to introduce and evaluate a dual filter that combines Radial Basis Function neural networks and Kalman filters to enhance the accuracy of numerical wave prediction models. Unlike the existing methods, which focus solely on systematic errors, the proposed framework concurrently targets both systematic and non-systematic parts of forecast errors, significantly reducing the bias and variability in significant wave height predictions. The produced filter is self-adaptive, identifying optimal Radial Basis Function network configurations through an automated process involving various network parameters tuning. The produced computational system is assessed using a time-window procedure applied across divergent time periods and regions in the Aegean Sea and the Pacific Ocean. The results reveal a consistent performance, outperforming classic Kalman filters with an average reduction of 53% in bias and 28% in RMSE, underlining the dual filter’s potential as a robust post-processing tool for environmental simulations.
ISSN:1424-8220
DOI:10.3390/s24248006
Fuente:Health & Medical Collection