Modeling and forecasting of TEC using subspace-based SSA-LRF-ANN model
Subspace-based signal processing methods are fundamentally pre-trained Artificial Neural Networks (ANN) that provide the basic structure for numerous computer vision applications and explore the most promising Earth Observation Applications (EOA). This paper examines the fundamentals of subspace-bas...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2025-07-01
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| Series: | Geodesy and Geodynamics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674984725000047 |
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| Summary: | Subspace-based signal processing methods are fundamentally pre-trained Artificial Neural Networks (ANN) that provide the basic structure for numerous computer vision applications and explore the most promising Earth Observation Applications (EOA). This paper examines the fundamentals of subspace-based methods and explores the most promising algorithm for forecasting ionospheric signal delays, which was designed explicitly regarding signal and noise subspaces. The learning efficiency derived from the subspace-based components of Singular Spectrum Analysis (SSA) significantly influences the implementation of Linear Recurrent Formula (LRF) and ANN models. The proposed study introduces a novel enhancement to LRF and ANN methodologies for Global Positioning System (GPS)– Total Electron Content (TEC) forecasts based on SSA. The GPS-derived TEC at Bangalore (13.02°N and 77.57°E) location grid during sunspot cycle 25 (2020) is considered for analysis. The SSA–LRF–ANN model demonstrates superior accuracy compared with the SSA–LRF, Autoregressive Moving Average (ARMA), and Holt–Winter (HW) models, achieving a correlation of 0.99, a Mean Absolute Error (MAE) of 0.55 TECU, a Mean Absolute Percentage Error (MAPE) of 7.06%, and a Root Mean Square Error (RMSE) of 0.75 TECU. Furthermore, the results and discussions section presents numerical illustrations that showcase the practical implementation of the SSA–LRF–ANN and its application. |
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| ISSN: | 1674-9847 |