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 |
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| Autor principal: | |
| Otros Autores: | , , , |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 1424-8220 | ||
| 024 | 7 | |a 10.3390/s24248006 |2 doi | |
| 035 | |a 3149751986 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 084 | |a 231630 |2 nlm | ||
| 100 | 1 | |a Donas, Athanasios |u Department of Electrical and Electronic Engineering, University of West Attica, Ancient Olive Grove Campus, 250, Thivon Ave., Egaleo, 12241 Athens, Greece; <email>adonas@uniwa.gr</email> (A.D.); <email>ikordatos@uniwa.gr</email> (I.K.); <email>ifamelis@uniwa.gr</email> (I.T.F.) | |
| 245 | 1 | |a A Dual Filter Based on Radial Basis Function Neural Networks and Kalman Filters with Application to Numerical Wave Prediction Models | |
| 260 | |b MDPI AG |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Machine learning | ||
| 653 | |a Accuracy | ||
| 653 | |a Weather forecasting | ||
| 653 | |a Datasets | ||
| 653 | |a Algorithms | ||
| 653 | |a Kalman filters | ||
| 653 | |a Neural networks | ||
| 653 | |a Data assimilation | ||
| 653 | |a Case studies | ||
| 653 | |a Bias | ||
| 700 | 1 | |a Kordatos, Ioannis |u Department of Electrical and Electronic Engineering, University of West Attica, Ancient Olive Grove Campus, 250, Thivon Ave., Egaleo, 12241 Athens, Greece; <email>adonas@uniwa.gr</email> (A.D.); <email>ikordatos@uniwa.gr</email> (I.K.); <email>ifamelis@uniwa.gr</email> (I.T.F.) | |
| 700 | 1 | |a Alexandridis, Alex |u Department of Electrical and Electronic Engineering, University of West Attica, Ancient Olive Grove Campus, 250, Thivon Ave., Egaleo, 12241 Athens, Greece; <email>adonas@uniwa.gr</email> (A.D.); <email>ikordatos@uniwa.gr</email> (I.K.); <email>ifamelis@uniwa.gr</email> (I.T.F.) | |
| 700 | 1 | |a Galanis, George |u Hellenic Naval Academy, Hatzikiriakion, 18539 Piraeus, Greece; <email>ggalanis@hna.gr</email> | |
| 700 | 1 | |a Famelis, Ioannis Th |u Department of Electrical and Electronic Engineering, University of West Attica, Ancient Olive Grove Campus, 250, Thivon Ave., Egaleo, 12241 Athens, Greece; <email>adonas@uniwa.gr</email> (A.D.); <email>ikordatos@uniwa.gr</email> (I.K.); <email>ifamelis@uniwa.gr</email> (I.T.F.) | |
| 773 | 0 | |t Sensors |g vol. 24, no. 24 (2024), p. 8006 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3149751986/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3149751986/fulltextwithgraphics/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3149751986/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |