GNSS Time-Series Prediction Using Moving Average Filter and Multi-Layer Perceptron Neuron Network

Abstract

The Mekong Delta and Ho Chi Minh City in Vietnam are recognized as areas significantly impacted by land subsidence. This phenomenon has led to notable consequences, including increased vulnerability to issues such as saline intrusion and tidal flooding. GNSS-CORS technology, known for its capability to provide continuous time-series data, plays a crucial role in accurately monitoring changes in the land surface. Despite the existence of traditional algorithms for analyzing continuous measurement data collected through GNSS-CORS technology, their effectiveness is constrained by challenges in handling diverse input data and limitations in forecasting future displacements. Consequently, there is a growing trend towards the adoption of artificial intelligence techniques, particularly artificial neural networks (ANN), for predicting Up component in GNSS time-serries daily solution. This study leverages data from the CTHO GNSS CORS station located in the Mekong Delta to evaluate proposed models. An innovative hybrid approach, which integrates the Moving Average Filter (MAF) and Multilayer Perceptron Neural Network (MLPNN), is introduced to enhance the accuracy of forecasting. Performance evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) are utilized to assess the effectiveness of the models. Results demonstrate the superior performance of the MLPNN model, achieving high prediction accuracy with metrics including MAE = 0.001, MSE = 0.000, and RMSE = 0.002. This research underscores the robustness of the proposed model in forecasting GNSS time-serries daily solution, highlighting its potential for practical applications in geodetic research.