Analyzing the Impact of Climate Factors on GNSS‐Derived Displacements by Combining the Extended Helmert Transformation and XGboost Machine Learning Algorithm

Liu, Hanlin; Yang, Linqiang; Li, Fulin

doi:10.1155/2021/9926442

Cited by 6 publications

(3 citation statements)

References 47 publications

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“…The presence of outliers in the measurements is inevitable and can negatively affect the performance and accuracy of a model if they are not addressed appropriately [30,[57][58][59]. In data pre-processing, the Interquartile Range (IQR) method is widely used to detect outliers.…”

Section: Data Pre-processingmentioning

confidence: 99%

Machine Learning-Based Estimation of Hourly GNSS Precipitable Water Vapour

Adavi,

Ghassemi,

Weber

et al. 2023

Remote Sensing

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Water vapour plays a key role in long-term climate studies and short-term weather forecasting. Therefore, to understand atmospheric variations, it is crucial to observe water vapour and its spatial distribution. In the current era, Global Navigation Satellite Systems (GNSS) are widely used to monitor this critical atmospheric component because GNSS signals pass through the atmosphere, allowing us to estimate water vapour at various locations and times. The amount of precipitable water vapour (PWV) is one of the most fascinating quantities, which provides meteorologists and climate scientists with valuable information. However, calculating PWV accurately from processing GNSS observations usually requires the input of further observed meteorological parameters with adequate quality and latency. To bypass this problem, hourly PWVs without meteorological parameters are computed using the Random Forest and Artificial Neural Network algorithms in this research. The first step towards this objective is establishing a regional weighted mean temperature model for Austria. To achieve this, measurements of radiosondes launched from different locations in Austria are employed. The results indicate that Random Forest is the most accurate method compared to regression (linear and polynomial), Artificial Neural Network, and empirical methods. PWV models are then developed using data from 39 GNSS stations that cover Austria’s entire territory. The models are afterwards tested under different atmospheric conditions with four radiosonde stations. Based on the obtained results, the Artificial Neural Network model with a single hidden layer slightly outperforms other investigated models, with only a 5% difference in mean absolute error. As a result, the hourly PWV can be estimated without relying on measured meteorological parameters with an average mean absolute error of less than 2.5 mm in Austria.

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Section: Data Pre-processingmentioning

confidence: 99%

Machine Learning-Based Estimation of Hourly GNSS Precipitable Water Vapour

Adavi,

Ghassemi,

Weber

et al. 2023

Remote Sensing

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“…In, ML models have been applied to other environmental remote sensing applications such as landslide monitoring/prediction, estimating nearshore water depths, weather forecast by monitoring and forecasting precipitable water vapor (PWV), and forecast hourly intense rainfall [11,[55][56][57][58][59][60][61][62].…”

Section: Earth Observation and Monitoringmentioning

confidence: 99%

A Systematic Review of Machine Learning Techniques for GNSS Use Cases

Siemuri

Selvan

Välisuo

et al. 2022

IEEE Trans. Aerosp. Electron. Syst.

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In terms of the availability and accuracy of positioning, navigation, and timing (PNT), the traditional Global Navigation Satellite System (GNSS) algorithms and models perform well under good signal conditions. In order to improve their robustness and performance in less than optimal signal environments, many researchers have proposed machine learning (ML) based GNSS models (ML models) as early as the 1990s. However, no study has been done in a systematic way to analyze the extent of the research on the utilization of ML models in GNSS and their performance. The aim of this research is to perform a systematic review of the type of ML models utilized in GNSS use cases, their performance with respect to accuracy, their comparison with other models (ML and non-ML), and their GNSS application context. In this study, we perform a systematic review of studies from 2000 to 2021 in the literature that utilizes machine learning techniques in GNSS use cases. We assess the performance of the machine learning techniques in the existing literature on their application to GNSS. Furthermore, the strengths and weaknesses of machine learning techniques are summarized. In this paper, we have identified 213 selected studies and ten categories of machine learning techniques. The results prove the acceptable performance of machine learning

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“…The VMD algorithm was proposed by Dragomiretskiy and Zosso (2014) and has been widely used and effectively improved in many fields of research (Sivavaraprasad et al 2017;Choi et al 2018;Wang et al 2019). The XGBoost algorithm proposed by Chen and Guestrin (2016) has been widely used but has rarely been applied to GNSS daily time series (Liu et al 2021;Jia et al 2021;Dey et al 2022). When we use the XGBoost model to construct regression problems, we need some correlated features, and the VMD algorithm can help us obtain low noise level time series.…”

Section: Introductionmentioning

confidence: 99%

Forecasting and analysing the GNSS vertical time series with an improved VMD-CXGBoost model

Li,

2023

Preprint

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Global Navigation Satellite System (GNSS) vertical time series studies can monitor crustal deformations and plate tectonics, contributing to the estimation of regional sea-level rise and detecting various geological hazards. This study proposes a new model to forecast and analyze the GNSS vertical time series. This model is based on a method to construct features using the variational mode decomposition (VMD) algorithm and includes a correction function to optimize the eXtreme Gradient Boosting (XGBoost) algorithm, called the VMD-CXGBoost model. To verify the validity of the VMD-CXGBoost model, six GNSS reference stations are selected within China. Compared with VMD-CNN-LSTM, the VMD-CXGBoost-derived forecasting RMSE and MAE are decreased by 20.76% and 23.23%, respectively. The flicker noise and white noise decrease by 15.43% and 25.65%, and the average trend difference is 1 mm/year, with a 15.14% reduction in uncertainty. Compared with the cubic spline interpolation method, the VMD-CXGBoost-derived interpolation RMSE is reduced by more than 40%. Therefore, the proposed VMD-CXGBoost model could be used as a powerful alternative tool to forecast GNSS vertical time series and will be of wide practical value in the fields of reference frame maintenance.

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Analyzing the Impact of Climate Factors on GNSS‐Derived Displacements by Combining the Extended Helmert Transformation and XGboost Machine Learning Algorithm

Cited by 6 publications

References 47 publications

Machine Learning-Based Estimation of Hourly GNSS Precipitable Water Vapour

Machine Learning-Based Estimation of Hourly GNSS Precipitable Water Vapour

A Systematic Review of Machine Learning Techniques for GNSS Use Cases

Forecasting and analysing the GNSS vertical time series with an improved VMD-CXGBoost model

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