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  • 2020-2024  (3)
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  • 1
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    Water quality prediction for Beijing’s sub-center based on deep learning model (2023)
    In:  XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
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    Publication Date: 2023-08-29
    Description: In the context of rapid urbanization, a certain amount of pollution discharge would inevitably produce in the process of urbanization construction. Therefore, it is necessary to establish a fast and effective method of water quality prediction for sustainable development. The deep learning methods could express the high-dimensional and nonlinear relationship between water quality and other factors, so the LSTM and BP models were established in this paper, then the transfer learning model was proposed and optimized on the base of the upstream and downstream relationships in the Beijing’s sub-center. The results showed that the transfer learning improved NSE by 7% and 9% for LSTM and BP at Dongguan Bridge, respectively. For the Xugezhuang in the Liangshui River, it improved by 11% and 17%, respectively. At Yulinzhuang, NSE were improved by 16% and 13%, respectively. The enhancement of the model performance is more obvious based on river structure, and it would provide an idea for the effective model construction in the ungauged basins or regions.
    Language: English
    Type: info:eu-repo/semantics/conferenceObject
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  • 2
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    Thank You to Our 2022 Peer Reviewers (2023)
    In:  Geophysical Research Letters
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    Publication Date: 2023-08-04
    Description: On behalf of the journal, AGU, and the scientific community, the editors of Geophysical Research Letters would like to sincerely thank those who reviewed manuscripts for us in 2022. The hours reading and commenting on manuscripts not only improve the manuscripts, but also increase the scientific rigor of future research in the field. With the advent of AGU's data policy, many reviewers have also helped immensely to evaluate the accessibility and availability of data, and many have provided insightful comments that helped to improve the data presentation and quality. We greatly appreciate the assistance of the reviewers in advancing open science, which is a key objective of AGU's data policy. We particularly appreciate the timely reviews in light of the demands imposed by the rapid review process at Geophysical Research Letters. We received 6,687 submissions in 2022 and 5,247 reviewers contributed to their evaluation by providing 8,720 reviews in total. We deeply appreciate their contributions in these challenging times.
    Type: info:eu-repo/semantics/article
    Format: application/pdf
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  • 3
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    Comparison of hourly satellite precipitation products in capturing extremes over Beijing (2023)
    In:  XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
    Details
    Publication Date: 2023-07-03
    Description: With intense climate change and rapid urbanization, extreme precipitation events have threatened the sustainable development of cities. Due to the advantages of wide spatial range, high spatiotemporal resolution and free download, satellite precipitation products have become a potential choice for extreme precipitation monitoring and hydrological simulation. For the application over different urban areas, it is important to investigate the performance of different satellite precipitation products. This study evaluated five hourly satellite precipitation products in capturing extremes: the Integrated Multi-satellite Retrievals for GPM (IMERG), Climate Prediction Center Morphing Technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), the Global Satellite Mapping of Precipitation (GSMaP) and Fengyun 2 Meteorological Satellite Series (FY2). Based on the observations in Beijing, statistical metrics, categorical skill metrics and extreme precipitation indices were selected and analyzed. Results showed that IMERG had the highest accuracy, and all five products underestimated extreme precipitation. The performance was well in summer while worse in winter. Our study would provide some significant reference for developers and potential users in Beijing or other similar regions.
    Language: English
    Type: info:eu-repo/semantics/conferenceObject
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