A Dual Filter Based on Radial Basis Function Neural Networks and Kalman Filters with Application to Numerical Wave Prediction Models
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 concurrent...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/24/8006 |
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| author | Athanasios Donas Ioannis Kordatos Alex Alexandridis George Galanis Ioannis Th. Famelis |
| author_facet | Athanasios Donas Ioannis Kordatos Alex Alexandridis George Galanis Ioannis Th. Famelis |
| author_sort | Athanasios Donas |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1c728aedd38c4bfaa6ae720b1aee86df |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-1c728aedd38c4bfaa6ae720b1aee86df2025-08-20T02:56:55ZengMDPI AGSensors1424-82202024-12-012424800610.3390/s24248006A Dual Filter Based on Radial Basis Function Neural Networks and Kalman Filters with Application to Numerical Wave Prediction ModelsAthanasios Donas0Ioannis Kordatos1Alex Alexandridis2George Galanis3Ioannis Th. Famelis4Department of Electrical and Electronic Engineering, University of West Attica, Ancient Olive Grove Campus, 250, Thivon Ave., Egaleo, 12241 Athens, GreeceDepartment of Electrical and Electronic Engineering, University of West Attica, Ancient Olive Grove Campus, 250, Thivon Ave., Egaleo, 12241 Athens, GreeceDepartment of Electrical and Electronic Engineering, University of West Attica, Ancient Olive Grove Campus, 250, Thivon Ave., Egaleo, 12241 Athens, GreeceHellenic Naval Academy, Hatzikiriakion, 18539 Piraeus, GreeceDepartment of Electrical and Electronic Engineering, University of West Attica, Ancient Olive Grove Campus, 250, Thivon Ave., Egaleo, 12241 Athens, GreeceThe 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.https://www.mdpi.com/1424-8220/24/24/8006Kalman filterspost-process algorithmsradial basis function neural networkssignificant wave heightWAM |
| spellingShingle | Athanasios Donas Ioannis Kordatos Alex Alexandridis George Galanis Ioannis Th. Famelis A Dual Filter Based on Radial Basis Function Neural Networks and Kalman Filters with Application to Numerical Wave Prediction Models Sensors Kalman filters post-process algorithms radial basis function neural networks significant wave height WAM |
| title | A Dual Filter Based on Radial Basis Function Neural Networks and Kalman Filters with Application to Numerical Wave Prediction Models |
| title_full | A Dual Filter Based on Radial Basis Function Neural Networks and Kalman Filters with Application to Numerical Wave Prediction Models |
| title_fullStr | A Dual Filter Based on Radial Basis Function Neural Networks and Kalman Filters with Application to Numerical Wave Prediction Models |
| title_full_unstemmed | A Dual Filter Based on Radial Basis Function Neural Networks and Kalman Filters with Application to Numerical Wave Prediction Models |
| title_short | A Dual Filter Based on Radial Basis Function Neural Networks and Kalman Filters with Application to Numerical Wave Prediction Models |
| title_sort | dual filter based on radial basis function neural networks and kalman filters with application to numerical wave prediction models |
| topic | Kalman filters post-process algorithms radial basis function neural networks significant wave height WAM |
| url | https://www.mdpi.com/1424-8220/24/24/8006 |
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