Zhu et al. (2025) A Multichannel CNN-LSTM-Based Prediction Model for Precipitable Water Vapor in a Region With a Single GNSS Station

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2025
• Date: 2025-12-31
• Authors: Dantong Zhu, Wang Li, Kefei Zhang, Qingfeng Hu, Peipei He, L J Zhang, Suqin Wu, Wei-Bo Yin, Minjie Gao, Longjiang Li
• DOI: 10.1109/jstars.2025.3649502

Research Groups

Not specified in the provided text.

Short Summary

This paper proposes a multichannel Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) based model to predict precipitable water vapor (PWV) using data from a single Global Navigation Satellite System (GNSS) station.

Objective

• To develop and evaluate a multichannel CNN-LSTM prediction model for precipitable water vapor in a region served by a single GNSS station.

Study Configuration

• Spatial Scale: A specific region with a single GNSS station, implying a localized or point-based analysis.
• Temporal Scale: Prediction model, suggesting forecasting of future PWV values, but the specific prediction horizon (e.g., hourly, daily) is not detailed.

Methodology and Data

• Models used: Multichannel Convolutional Neural Network (CNN) combined with Long Short-Term Memory (LSTM) neural networks.
• Data sources: Data primarily from a single Global Navigation Satellite System (GNSS) station, used to derive or inform precipitable water vapor measurements. Other potential input channels for the multichannel model are not specified.

Main Results

The provided text does not contain specific results. The paper aims to demonstrate the effectiveness of the proposed CNN-LSTM model for PWV prediction.

Contributions

The provided text does not contain specific contributions. It is implied that the contribution lies in the application and potential efficacy of a multichannel CNN-LSTM architecture for PWV prediction using limited (single GNSS station) input.

Funding

Not specified in the provided text.

Citation

@article{Zhu2025Multichannel,
  author = {Zhu, Dantong and Li, Wang and Zhang, Kefei and Hu, Qingfeng and He, Peipei and Zhang, L J and Wu, Suqin and Yin, Wei-Bo and Gao, Minjie and Li, Longjiang},
  title = {A Multichannel CNN-LSTM-Based Prediction Model for Precipitable Water Vapor in a Region With a Single GNSS Station},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2025},
  doi = {10.1109/jstars.2025.3649502},
  url = {https://doi.org/10.1109/jstars.2025.3649502}
}