Ensemble based deep learning model for prediction of integrated water vapor (IWV) using GPS and meteorological observations
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Nirmala Bai Jadala
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Devanaboyina Venkata Ratnam
Abstract
Integrated water vapor (IWV) has been widely perceived through machine learning (ML) strategies. During this investigation, we employed IWV time series from weather stations to determine the oscillations and patterns with IWV across two latitudes namely VBIT, Hyderabad (India) and PWVUO station, Oregon (US). The GPS derived IWV and meteorological data such as pressure (P), temperature (T) and relative humidity (RH) dataset for the year 2014 has been taken from VBIT station and from PWVUO station for 2020. Five machine learning algorithms namely Optimized Ensemble (OE) model, Rational Quadratic Gaussian Process Regression model (RQ-GPR), Neural Networks model (NN), Cubic Support Vector Machine (CSVM) and Quadratic Support Vector Machine (QSVM) algorithms are used. The GPS derived IWV data revealed the maximum variation during summer monsoon period specifically in the month of July. The correlation analysis between GPS-IWV and optimized ensemble technique showed the highest correlation for the VBIT station with correlation coefficient as (ρ) = 99 % and at PWVUO station as (ρ) = 88 % for two different datasets. The residual analysis has also showed less variation to the optimized ensemble model. The performance metrics obtained for OE at VBIT station are mean absolute error (MAE) as 0.64 kg/m2, mean absolute percentage error (MAPE) as 3.80 % and root mean squared error (RMSE) as 0.94 kg/m2 and at PWVUO station the values are MAE = 1.91 kg/m2, MAPE = 11.76 % and RMSE as 1.97 kg/m2, respectively. The results explained that the OE method has shown a better performance compared to the remaining models.
Acknowledgements
We are thankful to University of Oregon (SRML), Eugene, Oregon, United states for providing GPS data. We are thankful to NARL, Gadanki, India for providing GPS data.
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Research ethics: Not applicable.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: The author states no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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© 2023 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Review
- A proposed neural network model for obtaining precipitable water vapor
- Original Research Articles
- Assessment of GNSS observations and positioning performance from non-flagship Android smartphones
- Dynamic mode decomposition and bivariate autoregressive short-term prediction of Earth rotation parameters
- Comparison of selected reliability optimization methods in application to the second order design of geodetic network
- Monte Carlo variance propagation for the uncertainty modeling of a kinematic LiDAR-based multi-sensor system
- Ensemble based deep learning model for prediction of integrated water vapor (IWV) using GPS and meteorological observations
- Keypoint-based registration of TLS point clouds using a statistical matching approach
- PPP_Mansoura: an open-source software for multi-constellation GNSS processing
- Ionospheric TEC prediction using FFNN during five different X Class solar flares of 2021 and 2022 and comparison with COKSM and IRI PLAS 2017
- Analysis of differential code biases for GPS receivers over the Indian region
- A machine-learning approach to estimate satellite-based position errors
- Monitoring of spatial displacements and deformation of hydraulic structures of hydroelectric power plants of the Dnipro and Dnister cascades (Ukraine)
Articles in the same Issue
- Frontmatter
- Review
- A proposed neural network model for obtaining precipitable water vapor
- Original Research Articles
- Assessment of GNSS observations and positioning performance from non-flagship Android smartphones
- Dynamic mode decomposition and bivariate autoregressive short-term prediction of Earth rotation parameters
- Comparison of selected reliability optimization methods in application to the second order design of geodetic network
- Monte Carlo variance propagation for the uncertainty modeling of a kinematic LiDAR-based multi-sensor system
- Ensemble based deep learning model for prediction of integrated water vapor (IWV) using GPS and meteorological observations
- Keypoint-based registration of TLS point clouds using a statistical matching approach
- PPP_Mansoura: an open-source software for multi-constellation GNSS processing
- Ionospheric TEC prediction using FFNN during five different X Class solar flares of 2021 and 2022 and comparison with COKSM and IRI PLAS 2017
- Analysis of differential code biases for GPS receivers over the Indian region
- A machine-learning approach to estimate satellite-based position errors
- Monitoring of spatial displacements and deformation of hydraulic structures of hydroelectric power plants of the Dnipro and Dnister cascades (Ukraine)
