Existing load forecasting methods typically assume that recent load data are available for prediction.This is not in conformity with reality since there is a time gap between the flow date(when power is consumed)and w...Existing load forecasting methods typically assume that recent load data are available for prediction.This is not in conformity with reality since there is a time gap between the flow date(when power is consumed)and when measurement values are obtained.To this end,this letter proposes an online learning-based probabilistic load forecasting method considering the impact of the data gap.Specifically,an adaptive ensemble backpropagation-enabled online quantile regression algorithm is developed to optimize the parameters of the attention network recursively using the newly obtained load observations.To further improve the reliability and sharpness of prediction intervals under significant data gaps,we introduce an online interval calibration technique.The proposed online learning method allows us to adaptively capture the dynamic changes in load patterns and alleviate the information lags caused by data gaps.Comparative tests utilizing real-world datasets reveal the superiority of the proposed method.展开更多
Background: Tropical forests play a fundamental role in the provision of diverse ecosystem services, such as biodiversity,climate and air quality regulation, freshwater provision, carbon cycling, agricultural support ...Background: Tropical forests play a fundamental role in the provision of diverse ecosystem services, such as biodiversity,climate and air quality regulation, freshwater provision, carbon cycling, agricultural support and culture. To understand the role of forests in the carbon balance, aboveground biomass(AGB) estimates are needed. Given the importance of Brazilian tropical forests, there is an urgent need to improve AGB estimates to support the Brazilian commitments under the United Nations Framework Convention on Climate Change(UNFCCC). Many AGB maps and datasets exist, varying in availability, scale and coverage. Thus, stakeholders, policy makers and scientists must decide which AGB product, dataset or combination of data to use for their particular goals. In this study, we assessed the gaps in the spatial AGB data across the Brazilian Amazon forests not only to orient the decision makers about the data that are currently available but also to provide a guide for future initiatives.Results: We obtained a map of the gaps in the forest AGB spatial data for the Brazilian Amazon using statistics and differences between AGB maps and a spatial multicriteria evaluation that considered the current AGB datasets. The AGB spatial data gap map represents areas with good coverage of AGB data and, consequently, the main gaps or priority areas where further biomass assessments should focus, including the northeast of Amazon State, Amapá and northeast of Pará. Additional y, by quantifying the variability in both the AGB maps and field data on multiple environmental factors,we provide valuable elements for understanding the current AGB data as a function of climate, soil, vegetation and geomorphology.Conclusions: The map of AGB data gaps could become a useful tool for policy makers and different stakeholders working on National Communications, Reducing Emissions from Deforestation and Degradation(REDD+), or carbon emissions modeling to prioritize places to implement further AGB assessments. Only 0.2% of the Amazon biome forest is sampled, and extensive effort is necessary to improve what we know about the tropical forest.展开更多
The Tahiti-Darwin Southern Oscillation index provided by Climate Analysis Center of USA has been used in numerous studies. But, it has some deficiency. It contains noise mainly due to high month-to-month variability. ...The Tahiti-Darwin Southern Oscillation index provided by Climate Analysis Center of USA has been used in numerous studies. But, it has some deficiency. It contains noise mainly due to high month-to-month variability. In order to reduce the level of noise in the SO index, this paper introduces a fully data-adaptive filter based on singular spectrum analysis. Another interesting aspect of the filter is that it can be used to fill data gaps of the SO index by an iterative process. Eventually, a noiseless long-period data series without any gaps is obtained.展开更多
Slow Slip Events(SSEs)are critical for understanding subduction zone tectonics and earthquake prediction;however their detection is challenged by low-magnitude-offsets and data gaps.To address these challenges,this pa...Slow Slip Events(SSEs)are critical for understanding subduction zone tectonics and earthquake prediction;however their detection is challenged by low-magnitude-offsets and data gaps.To address these challenges,this paper introduces an optimization-based signal decomposition(OSD)fra mework capable of automatically processing signals with missing data.We applied and validated this framework with GNSS coordinate time series in the Cascadia subduction zone,benchmarking its perfo rmance against the existing SSEs catalog.The proposed high-magnitude-offset detection method achieved an accuracy of67.21%in single-station SSE detection,significantly outperforming traditional methods such as the Relative Strength Index(RSI;32.24%)and deep learning methods like bidirectional Long Short-Term Memory(bi-LSTM;44.41%).Additionally,we proposed a complementary velocity-based screening strategy that successfully identified low-magnitude-offset SSEs and events obscured by data gaps.Through cluster analysis of single-station detection results,we successfully identified the spatiotemporal boundary of the majority of SSEs.Finally,we established an anomaly catalog for uncataloged period from 2018 to 2024,which further demonstrates the method's efficacy in characterizing the spatiotemporal features of SSEs.The OSD-based SSEs detection framework identified SSEs with diverse kinematic patterns using raw geodetic data,facilitating the construction of high-quality SSEs catalogs.These advancements enhance our understanding of subduction zone dynamics and provide a robust technical foundation for seismic hazard assessment.展开更多
The primary mission of the Gravity Recovery and Climate Experiment (GRACE) satellite and its successor,GRACE Follow-On (GRACE-FO), is to provide time-variable gravity fields, and its observations have been widely used...The primary mission of the Gravity Recovery and Climate Experiment (GRACE) satellite and its successor,GRACE Follow-On (GRACE-FO), is to provide time-variable gravity fields, and its observations have been widely used in various studies. However, the nearly one-year gap between GRACE and GRACE-FO has affected our ability to obtain continuous time-variable gravity data. In this study, we use the Singular Spectrum Analysis (SSA) method to fill the nearly one-year gap between the GRACE and GRACE-FO missions, as well as the gaps within the GRACE mission itself, to generate a continuous and complete mascon product from April 2002 to December 2022. These products are evaluated at the basin scale in Greenland, Antarctica, and ten river basins worldwide, as well as across oceans. The results show that our filled data can effectively recover seasonal and interannual signals and exhibit good consistency with previous reconstructions. The products provided in this study will benefit GRACE applications related to oceans, glaciers, and terrestrial water storage.展开更多
Recurrent event gap times data frequently arise in biomedical studies and often more than one type of event is of interest. To evaluate the effects of covariates on the marginal recurrent event hazards functions, ther...Recurrent event gap times data frequently arise in biomedical studies and often more than one type of event is of interest. To evaluate the effects of covariates on the marginal recurrent event hazards functions, there exist two types of hazards models： the multiplicative hazards model and the additive hazards model. In the paper, we propose a more flexible additive-multiplicative hazards model for multiple type of recurrent gap times data, wherein some covariates are assumed to be additive while others are multiplicative. An estimating equation approach is presented to estimate the regression parameters. We establish asymptotic properties of the proposed estimators.展开更多
Time series from fisheries often contain multiple missing data.This is a severe limitation that prevents using the data for research on population dynamics,stock assessment,forecasting,and,hence,decision-making around...Time series from fisheries often contain multiple missing data.This is a severe limitation that prevents using the data for research on population dynamics,stock assessment,forecasting,and,hence,decision-making around marine resources.Several methods have been proposed to impute missing data in univariate time series.Still,their performances depend not only on the amount of missing data but also on the data structure.This study compares the performance of twelve imputation methods on the time series of marine fishery landings for six species in the Colombian Pacific Ocean.Unlike other studies,we validate the precision of the imputations in the same target time series that include missing data,using the Known Sub-Sequence Algorithm(KSSA),a novelty validation approach that simulates missing data in known sub-sequences of the target time series.The results showed that the best methods for imputation are Seasonal Decomposition with Kalman filters and Structural Models with Kalman filters fitted by maximum likelihood.Results also show that validating the imputation methods with other time series different to the target time series,leads to wrong imputation methods choices.It is noteworthy that these methods and also the validation framework are mainly suited to time series with non-random distribution of missing data,this is,missing data produced systematically in chunks or clusters with predictable frequency,which are common in marine sciences.展开更多
International disaster databases and catalogs provide a baseline for researchers,governments,communities,and organizations to understand the risk of a particular place,analyze broader trends in disaster risk,and justi...International disaster databases and catalogs provide a baseline for researchers,governments,communities,and organizations to understand the risk of a particular place,analyze broader trends in disaster risk,and justify investments in mitigation.Perhaps because Singapore is routinely identified as one of the safest countries in the world,Singapore’s past disasters have not been studied extensively with few events captured in major global databases such as EM-DAT.In this article,we fill the disaster data gap for postwar Singapore(1950–2020)using specified metrics through an archival search,review of literature,and analysis of secondary sources.We present four key lessons from cataloging these events.First,we expand Singapore’s disaster catalog to 39 events in this time period and quantify the extent of this data gap.Second,we identify the mitigating actions that have followed past events that contribute to Singapore’s present-day safety.Third,we discuss how these past events uncover continuities among vulnerability bearers in Singapore.Last,we identify limitations of a disaster catalog when considering future risks.In expanding the disaster catalog,this case study of Singapore supports the need for comprehensive understanding of past disasters in order to examine current and future disaster resilience.展开更多
The growing demand for high-quality,temporally consistent satellite imagery for environmental monitoring and land use research has exposed a substantial data gap in China.Unlike the United States,which provides Analys...The growing demand for high-quality,temporally consistent satellite imagery for environmental monitoring and land use research has exposed a substantial data gap in China.Unlike the United States,which provides Analysis Ready Data(ARD)for Landsat imagery,Chinese researchers currently lack an equivalent resource,resulting in time-intensive data processing and potential research inaccuracies.In this study,we introduce the first seamless,annual Leaf-On Landsat composite data cube for China,covering 1985 to 2023.Leveraging the comprehensive image compositing approach,our dataset harmonizes images across multiple Landsat sensors and addresses key challenges such as cloud and shadow contamination,reflectance consistency,and the data gaps.Over this period,an average of 7.9% of data remained unavailable due to cloud/shadow cover and limited data accessibility.To address this,we applied segmented linear interpolation to generate proxies,which we validated for stability,achieving high consistency with actual Landsat references for both stable and dynamic pixel sequences(r=0.77 to 0.99,root mean square error[RMSE]=0.0043 to 0.0232).Additionally,representativeness assessments indicate a strong correlation between our composites and Landsat reference images(closest to day of year 225)(r=0.75 to 0.94,RMSE=0.025 to 0.063),confirming that these composites effectively capture seasonal vegetation conditions across diverse land cover types.This dataset is expected to help reduce preprocessing efforts for researchers and provide a solid basis for land use monitoring and environmental assessments across China.展开更多
基金supported in part by National Natural Science Foundation of China under Grant 72401055in part by National Natural Science Foundation of China under Grant 52277083in part by the joint founding of Guangdong,and Dongguan under Grant 2023A1515110939.
文摘Existing load forecasting methods typically assume that recent load data are available for prediction.This is not in conformity with reality since there is a time gap between the flow date(when power is consumed)and when measurement values are obtained.To this end,this letter proposes an online learning-based probabilistic load forecasting method considering the impact of the data gap.Specifically,an adaptive ensemble backpropagation-enabled online quantile regression algorithm is developed to optimize the parameters of the attention network recursively using the newly obtained load observations.To further improve the reliability and sharpness of prediction intervals under significant data gaps,we introduce an online interval calibration technique.The proposed online learning method allows us to adaptively capture the dynamic changes in load patterns and alleviate the information lags caused by data gaps.Comparative tests utilizing real-world datasets reveal the superiority of the proposed method.
基金part of the Sao Paulo Research Foundation (FAPESP) Grant No.2013/20616–6 and 2018/18493–7the project LiDAR Remote Sensing of Brazilian Amazon Forests:Analysis of Forest Biomass,Forest Degradation,and Secondary Regrowth funded by the USAID Prime Award Number AID-OAA-A-11-00012。
文摘Background: Tropical forests play a fundamental role in the provision of diverse ecosystem services, such as biodiversity,climate and air quality regulation, freshwater provision, carbon cycling, agricultural support and culture. To understand the role of forests in the carbon balance, aboveground biomass(AGB) estimates are needed. Given the importance of Brazilian tropical forests, there is an urgent need to improve AGB estimates to support the Brazilian commitments under the United Nations Framework Convention on Climate Change(UNFCCC). Many AGB maps and datasets exist, varying in availability, scale and coverage. Thus, stakeholders, policy makers and scientists must decide which AGB product, dataset or combination of data to use for their particular goals. In this study, we assessed the gaps in the spatial AGB data across the Brazilian Amazon forests not only to orient the decision makers about the data that are currently available but also to provide a guide for future initiatives.Results: We obtained a map of the gaps in the forest AGB spatial data for the Brazilian Amazon using statistics and differences between AGB maps and a spatial multicriteria evaluation that considered the current AGB datasets. The AGB spatial data gap map represents areas with good coverage of AGB data and, consequently, the main gaps or priority areas where further biomass assessments should focus, including the northeast of Amazon State, Amapá and northeast of Pará. Additional y, by quantifying the variability in both the AGB maps and field data on multiple environmental factors,we provide valuable elements for understanding the current AGB data as a function of climate, soil, vegetation and geomorphology.Conclusions: The map of AGB data gaps could become a useful tool for policy makers and different stakeholders working on National Communications, Reducing Emissions from Deforestation and Degradation(REDD+), or carbon emissions modeling to prioritize places to implement further AGB assessments. Only 0.2% of the Amazon biome forest is sampled, and extensive effort is necessary to improve what we know about the tropical forest.
文摘The Tahiti-Darwin Southern Oscillation index provided by Climate Analysis Center of USA has been used in numerous studies. But, it has some deficiency. It contains noise mainly due to high month-to-month variability. In order to reduce the level of noise in the SO index, this paper introduces a fully data-adaptive filter based on singular spectrum analysis. Another interesting aspect of the filter is that it can be used to fill data gaps of the SO index by an iterative process. Eventually, a noiseless long-period data series without any gaps is obtained.
基金supported by the National Natural Science Foundation of China(Grant No.42274035)the Major Program(JD)of Hubei Province(Grant No.2023BAA026)the Hunan Provincial Land Surveying and Mapping Project(HNGTCH-2023-05)。
文摘Slow Slip Events(SSEs)are critical for understanding subduction zone tectonics and earthquake prediction;however their detection is challenged by low-magnitude-offsets and data gaps.To address these challenges,this paper introduces an optimization-based signal decomposition(OSD)fra mework capable of automatically processing signals with missing data.We applied and validated this framework with GNSS coordinate time series in the Cascadia subduction zone,benchmarking its perfo rmance against the existing SSEs catalog.The proposed high-magnitude-offset detection method achieved an accuracy of67.21%in single-station SSE detection,significantly outperforming traditional methods such as the Relative Strength Index(RSI;32.24%)and deep learning methods like bidirectional Long Short-Term Memory(bi-LSTM;44.41%).Additionally,we proposed a complementary velocity-based screening strategy that successfully identified low-magnitude-offset SSEs and events obscured by data gaps.Through cluster analysis of single-station detection results,we successfully identified the spatiotemporal boundary of the majority of SSEs.Finally,we established an anomaly catalog for uncataloged period from 2018 to 2024,which further demonstrates the method's efficacy in characterizing the spatiotemporal features of SSEs.The OSD-based SSEs detection framework identified SSEs with diverse kinematic patterns using raw geodetic data,facilitating the construction of high-quality SSEs catalogs.These advancements enhance our understanding of subduction zone dynamics and provide a robust technical foundation for seismic hazard assessment.
基金the National Natural Science Foundation of China(E3ER0402A2,E421040401)the University of Chinese Academy of Sciences Research Start-up Grant(110400M003)the Fundamental Research Funds for the Central Universities(E2ET0411X2).
文摘The primary mission of the Gravity Recovery and Climate Experiment (GRACE) satellite and its successor,GRACE Follow-On (GRACE-FO), is to provide time-variable gravity fields, and its observations have been widely used in various studies. However, the nearly one-year gap between GRACE and GRACE-FO has affected our ability to obtain continuous time-variable gravity data. In this study, we use the Singular Spectrum Analysis (SSA) method to fill the nearly one-year gap between the GRACE and GRACE-FO missions, as well as the gaps within the GRACE mission itself, to generate a continuous and complete mascon product from April 2002 to December 2022. These products are evaluated at the basin scale in Greenland, Antarctica, and ten river basins worldwide, as well as across oceans. The results show that our filled data can effectively recover seasonal and interannual signals and exhibit good consistency with previous reconstructions. The products provided in this study will benefit GRACE applications related to oceans, glaciers, and terrestrial water storage.
基金The Science Foundation(JA12301)of Fujian Educational Committeethe Teaching Quality Project(ZL0902/TZ(SJ))of Higher Education in Fujian Provincial Education Department
文摘Recurrent event gap times data frequently arise in biomedical studies and often more than one type of event is of interest. To evaluate the effects of covariates on the marginal recurrent event hazards functions, there exist two types of hazards models： the multiplicative hazards model and the additive hazards model. In the paper, we propose a more flexible additive-multiplicative hazards model for multiple type of recurrent gap times data, wherein some covariates are assumed to be additive while others are multiplicative. An estimating equation approach is presented to estimate the regression parameters. We establish asymptotic properties of the proposed estimators.
文摘Time series from fisheries often contain multiple missing data.This is a severe limitation that prevents using the data for research on population dynamics,stock assessment,forecasting,and,hence,decision-making around marine resources.Several methods have been proposed to impute missing data in univariate time series.Still,their performances depend not only on the amount of missing data but also on the data structure.This study compares the performance of twelve imputation methods on the time series of marine fishery landings for six species in the Colombian Pacific Ocean.Unlike other studies,we validate the precision of the imputations in the same target time series that include missing data,using the Known Sub-Sequence Algorithm(KSSA),a novelty validation approach that simulates missing data in known sub-sequences of the target time series.The results showed that the best methods for imputation are Seasonal Decomposition with Kalman filters and Structural Models with Kalman filters fitted by maximum likelihood.Results also show that validating the imputation methods with other time series different to the target time series,leads to wrong imputation methods choices.It is noteworthy that these methods and also the validation framework are mainly suited to time series with non-random distribution of missing data,this is,missing data produced systematically in chunks or clusters with predictable frequency,which are common in marine sciences.
基金We would like to acknowledge support from the National Research Foundation,Prime Minister's Office,Singapore under the NRF2018-SR2001-007 and NRF-NRFF2018-06 awardsThis research is also partly supported by the National Research Foundation Singaporethe Singapore Ministry of Education under the Research Centres of Excellence initiative through the Earth Observatory of Singapore
文摘International disaster databases and catalogs provide a baseline for researchers,governments,communities,and organizations to understand the risk of a particular place,analyze broader trends in disaster risk,and justify investments in mitigation.Perhaps because Singapore is routinely identified as one of the safest countries in the world,Singapore’s past disasters have not been studied extensively with few events captured in major global databases such as EM-DAT.In this article,we fill the disaster data gap for postwar Singapore(1950–2020)using specified metrics through an archival search,review of literature,and analysis of secondary sources.We present four key lessons from cataloging these events.First,we expand Singapore’s disaster catalog to 39 events in this time period and quantify the extent of this data gap.Second,we identify the mitigating actions that have followed past events that contribute to Singapore’s present-day safety.Third,we discuss how these past events uncover continuities among vulnerability bearers in Singapore.Last,we identify limitations of a disaster catalog when considering future risks.In expanding the disaster catalog,this case study of Singapore supports the need for comprehensive understanding of past disasters in order to examine current and future disaster resilience.
基金supported in part by the National Science Foundation for Distinguished Young Scholars of China under Grant 42225107the National Key Research and Development Program of China under Grant 2022YFB3903402the National Natural Science Foundation of China under Grant 42171409 and Grant 42171410.
文摘The growing demand for high-quality,temporally consistent satellite imagery for environmental monitoring and land use research has exposed a substantial data gap in China.Unlike the United States,which provides Analysis Ready Data(ARD)for Landsat imagery,Chinese researchers currently lack an equivalent resource,resulting in time-intensive data processing and potential research inaccuracies.In this study,we introduce the first seamless,annual Leaf-On Landsat composite data cube for China,covering 1985 to 2023.Leveraging the comprehensive image compositing approach,our dataset harmonizes images across multiple Landsat sensors and addresses key challenges such as cloud and shadow contamination,reflectance consistency,and the data gaps.Over this period,an average of 7.9% of data remained unavailable due to cloud/shadow cover and limited data accessibility.To address this,we applied segmented linear interpolation to generate proxies,which we validated for stability,achieving high consistency with actual Landsat references for both stable and dynamic pixel sequences(r=0.77 to 0.99,root mean square error[RMSE]=0.0043 to 0.0232).Additionally,representativeness assessments indicate a strong correlation between our composites and Landsat reference images(closest to day of year 225)(r=0.75 to 0.94,RMSE=0.025 to 0.063),confirming that these composites effectively capture seasonal vegetation conditions across diverse land cover types.This dataset is expected to help reduce preprocessing efforts for researchers and provide a solid basis for land use monitoring and environmental assessments across China.
