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  • Xu, Lei  (7)
  • English  (7)
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  • 1
    Online Resource
    Online Resource
    Spatiotemporal forecasting in earth system science: Methods, uncertainties, predictability and future directions
    Elsevier BV ; 2021
    In:  Earth-Science Reviews Vol. 222 ( 2021-11), p. 103828-
    Details
    In: Earth-Science Reviews, Elsevier BV, Vol. 222 ( 2021-11), p. 103828-
    Type of Medium: Online Resource
    ISSN: 0012-8252
    URL: Article
    DOI: 10.1016/j.earscirev.2021.103828
    RVK:
    RA 1000
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2021
    detail.hit.zdb_id: 1792-9
    detail.hit.zdb_id: 2012642-6
    SSG: 13
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  • 2
    Online Resource
    Online Resource
    A parametric multivariate drought index for drought monitoring and assessment under climate change
    Elsevier BV ; 2021
    In:  Agricultural and Forest Meteorology Vol. 310 ( 2021-11), p. 108657-
    Details
    In: Agricultural and Forest Meteorology, Elsevier BV, Vol. 310 ( 2021-11), p. 108657-
    Type of Medium: Online Resource
    ISSN: 0168-1923
    URL: Article
    DOI: 10.1016/j.agrformet.2021.108657
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2021
    detail.hit.zdb_id: 2012165-9
    SSG: 23
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  • 3
    Online Resource
    Online Resource
    Hybrid Deep Learning and S2S Model for Improved Sub-Seasonal Surface and Root-Zone Soil Moisture Forecasting
    MDPI AG ; 2023
    In:  Remote Sensing Vol. 15, No. 13 ( 2023-07-05), p. 3410-
    Details
    In: Remote Sensing, MDPI AG, Vol. 15, No. 13 ( 2023-07-05), p. 3410-
    Abstract: Surface soil moisture (SSM) and root-zone soil moisture (RZSM) are key hydrological variables for the agricultural water cycle and vegetation growth. Accurate SSM and RZSM forecasting at sub-seasonal scales would be valuable for agricultural water management and preparations. Currently, weather model-based soil moisture predictions are subject to large uncertainties due to inaccurate initial conditions and empirical parameterization schemes, while the data-driven machine learning methods have limitations in modeling long-term temporal dependences of SSM and RZSM because of the lack of considerations in the soil water process. Thus, here, we innovatively integrate the model-based soil moisture predictions from a sub-seasonal-to-seasonal (S2S) model into a data-driven stacked deep learning model to construct a hybrid SSM and RZSM forecasting framework. The hybrid forecasting model is evaluated over the Yangtze River Basin and parts of Europe from 1- to 46-day lead times and is compared with four baseline methods, including the support vector regression (SVR), random forest (RF), convolutional long short-term memory (ConvLSTM) and the S2S model. The results indicate substantial skill improvements in the hybrid model relative to baseline models over the two study areas spatiotemporally, in terms of the correlation coefficient, unbiased root mean square error (ubRMSE) and RMSE. The hybrid forecasting model benefits from the long-lead predictive skill from S2S and retains the advantages of data-driven soil moisture memory modeling at short-lead scales, which account for the superiority of hybrid forecasting. Overall, the developed hybrid model is promising for improved sub-seasonal SSM and RZSM forecasting over global and local areas.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs15133410
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2513863-7
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  • 4
    Online Resource
    Online Resource
    Quantifying the uncertainty of precipitation forecasting using probabilistic deep learning
    Copernicus GmbH ; 2022
    In:  Hydrology and Earth System Sciences Vol. 26, No. 11 ( 2022-06-14), p. 2923-2938
    Details
    In: Hydrology and Earth System Sciences, Copernicus GmbH, Vol. 26, No. 11 ( 2022-06-14), p. 2923-2938
    Abstract: Abstract. Precipitation forecasting is an important mission in weather science. In recent years, data-driven precipitation forecasting techniques could complement numerical prediction, such as precipitation nowcasting, monthly precipitation projection and extreme precipitation event identification. In data-driven precipitation forecasting, the predictive uncertainty arises mainly from data and model uncertainties. Current deep learning forecasting methods could model the parametric uncertainty by random sampling from the parameters. However, the data uncertainty is usually ignored in the forecasting process and the derivation of predictive uncertainty is incomplete. In this study, the input data uncertainty, target data uncertainty and model uncertainty are jointly modeled in a deep learning precipitation forecasting framework to estimate the predictive uncertainty. Specifically, the data uncertainty is estimated a priori and the input uncertainty is propagated forward through model weights according to the law of error propagation. The model uncertainty is considered by sampling from the parameters and is coupled with input and target data uncertainties in the objective function during the training process. Finally, the predictive uncertainty is produced by propagating the input uncertainty in the testing process. The experimental results indicate that the proposed joint uncertainty modeling framework for precipitation forecasting exhibits better forecasting accuracy (improving RMSE by 1 %–2 % and R2 by 1 %–7 % on average) relative to several existing methods, and could reduce the predictive uncertainty by ∼28 % relative to the approach of Loquercio et al. (2020). The incorporation of data uncertainty in the objective function changes the distributions of model weights of the forecasting model and the proposed method can slightly smooth the model weights, leading to the reduction of predictive uncertainty relative to the method of Loquercio et al. (2020). The predictive accuracy is improved in the proposed method by incorporating the target data uncertainty and reducing the forecasting error of extreme precipitation. The developed joint uncertainty modeling method can be regarded as a general uncertainty modeling approach to estimate predictive uncertainty from data and model in forecasting applications.
    Type of Medium: Online Resource
    ISSN: 1607-7938
    URL: Article
    DOI: 10.5194/hess-26-2923-2022
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2022
    detail.hit.zdb_id: 2100610-6
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  • 5
    Online Resource
    Online Resource
    A review of ship collision risk assessment, hotspot detection and path planning for maritime traffic control in restricted waters
    Cambridge University Press (CUP) ; 2022
    In:  Journal of Navigation Vol. 75, No. 6 ( 2022-11), p. 1337-1363
    Details
    In: Journal of Navigation, Cambridge University Press (CUP), Vol. 75, No. 6 ( 2022-11), p. 1337-1363
    Abstract: The three research topics, ship collision risk assessment, ship traffic hotspot detection and prediction, and collision-avoidance based ship path planning, are vital for next-generation vessel traffic management and monitoring systems. The system development is closely related to big data analytics and artificial intelligence for restricted waters. This study, therefore, aims to analyse the state-of-the art of these three topics over the latest decade, identify research gaps, and shed light on future research avenues. To achieve these three objectives, we critically and systematically review related articles that were published during the period between 2011 and 2021. We believe that this comprehensive and critical literature review would have a significant and profound impact on the formal safety assessment and vessel traffic management, and monitoring studies because it is not only an extension but also an essential continuity work of the literature review on maritime waterway risk assessment and prediction, as well as ship path guidance for ship collision risk mitigation in accordance with current automation vessels development and modern intelligent port construction.
    Type of Medium: Online Resource
    ISSN: 0373-4633 , 1469-7785
    URL: Article
    DOI: 10.1017/S0373463322000650
    Language: English
    Publisher: Cambridge University Press (CUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2015312-0
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  • 6
    Online Resource
    Online Resource
    Monthly Ocean Primary Productivity Forecasting by Joint Use of Seasonal Climate Prediction and Temporal Memory
    MDPI AG ; 2023
    In:  Remote Sensing Vol. 15, No. 5 ( 2023-03-02), p. 1417-
    Details
    In: Remote Sensing, MDPI AG, Vol. 15, No. 5 ( 2023-03-02), p. 1417-
    Abstract: Ocean primary productivity generated by phytoplankton is critical for ocean ecosystems and the global carbon cycle. Accurate ocean primary productivity forecasting months in advance is beneficial for marine management. Previous persistence-based prediction studies ignore the temporal memories of multiple relevant factors and the seasonal forecasting skill drops quickly with increasing lead time. On the other hand, the emerging ensemble climate forecasts are not well considered as new predictability sources of ocean conditions. Here we proposed a joint forecasting model by combining the seasonal climate predictions from ten heterogeneous models and the temporal memories of relevant factors to examine the monthly predictability of ocean productivity from 0.5- to 11.5-month lead times. The results indicate that a total of ~90% and ~20% productive oceans are expected to be skillfully predicted by the combination of seasonal SST predictions and local memory at 0.5- and 4.5-month leads, respectively. The joint forecasting model improves by 10% of the skillfully predicted areas at 6.5-month lead relative to the prediction by productivity persistence. The hybrid data-driven and model-driven forecasting approach improves the predictability of ocean productivity relative to individual predictions, of which the seasonal climate predictions contribute largely to the skill improvement over the equatorial Pacific and Indian Ocean. These findings highlight the advantages of the integration of climate predictions and temporal memory for ocean productivity forecasting and may provide useful seasonal forecasting information for ocean ecosystem management.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs15051417
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2513863-7
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  • 7
    Online Resource
    Online Resource
    Evaluating Competitiveness of Container Shipping Operators in the Sustainability and Digitalization Era
    MDPI AG ; 2024
    In:  Sustainability Vol. 16, No. 10 ( 2024-05-17), p. 4228-
    Details
    In: Sustainability, MDPI AG, Vol. 16, No. 10 ( 2024-05-17), p. 4228-
    Abstract: This study aims to comprehensively analyze the operational status and international competitiveness of global container shipping enterprises in the era of sustainability and digitalization within the field of international container transportation. Utilizing the entropy method, this study quantitatively evaluates the direct operational strength of 14 leading container shipping enterprises while considering multiple factors including environmental protection, service quality, company scale, customer satisfaction, research and development level, and brand influence. The contributions of this study to the existing knowledge are primarily manifested in several aspects: firstly, by constructing a comprehensive evaluation framework, it offers a new perspective for assessing the international competitiveness of container shipping enterprises, facilitating a more comprehensive understanding of their strengths and weaknesses. Secondly, this study emphasizes the crucial roles of environmental protection and customer service in the competitiveness of shipping enterprises, providing new strategic directions for the industry’s sustainable development and digital transformation. Lastly, through detailed analysis of the operational performance of different companies, this study provides specific improvement suggestions for shipping enterprises, aiding them in achieving more precise management and more efficient development. The research findings demonstrate that companies exhibit varied performance in different aspects, showcasing their respective strengths and challenges. Particularly, this study identifies leading enterprises that have made significant progress in environmental technology innovation and customer service, while also highlighting deficiencies in some companies regarding scale expansion and brand building. These findings not only offer valuable insights for the development of the shipping industry but also serve as a window for policymakers, investors, and consumers to gain a deeper understanding of the shipping market. Through the thorough analysis conducted in this study, we aim to contribute to the sustainable development and digital transformation of the global container shipping industry.
    Type of Medium: Online Resource
    ISSN: 2071-1050
    URL: Article
    DOI: 10.3390/su16104228
    Language: English
    Publisher: MDPI AG
    Publication Date: 2024
    detail.hit.zdb_id: 2518383-7
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