NSTL国家科技图书文献中心

1. Extracting Daily Aggregate Load Profiles from Monthly Consumption NSTL国家科技图书文献中心

Anmol Saraf | Anupama Kowli - 《Energy Informatics,Part I》 - Energy Informatics Academy Conference - 2025, - 332～348 - 共17页

摘要： how fine-grained smart meter data and monthly | profile generation is proposed which only uses monthly | Consumer load profiling involves examining | patterns of energy consumption using available data. With | smart meter data available at (sub-)hourly intervals

关键词： Load profile generation | Monthly electricity data | Multi-layer perceptron | Random forest | XGboost

2. Fourier‐Series‐Based Application Modeling for Monthly Hydropower Generation NSTL国家科技图书文献中心

Zong-chang Yang - 《Computer applications in engineering education》 - 2025,33(2) - e70025.1～e70025.11 - 共11页

摘要： monthly hydropower generation, this study outlines an | for evaluating monthly* hydropower generation to* | and quasiperiodic signal identification for monthly | As one classical utility for timeseries | analyzing, the Fourierseries (FS) technique has been

关键词： CBS | engineering education | FS | hydropower

3. Filling ionospheric monthly medians missing data: a machine learning approach NSTL国家科技图书文献中心

Zossi, Bruno S. | Medina, Franco D.... - 《Proceedings of the Royal Society,A.Mathematical, physical and engineering sciences》 - 2025,481(2308) - 共16页

摘要： missing values in the hourly monthly* median foF2* | Ionospheric databases, like most time series | of experimental parameters, contain missing data | owing to either natural or technical reasons, limiting | the statistical methods that can be used to analyse

关键词： machine learning | ionosphere | random forest | gradient boosting | missing data

4. Enhancing monthly precipitation forecasting by integrating multi-source data with machine learning models: a study in the Upper Blue Nile Basin NSTL国家科技图书文献中心

Mohammed, Juhar | Mengiste, Yenesew ... - 《Modeling Earth Systems and Environment》 - 2025,11(1) - 共16页

摘要： improve monthly* precipitation forecasts by integrating* | monthly precipitation forecasts. All models exhibited | enhance monthly* precipitation forecasts, providing* | This study developed a novel framework to | multi-source data with machine-learning techniques

关键词： Multi-source data integration | Machine learning models | Monthly precipitation forecasting | Global precipitation predictions | Upper Blue Nile Basin

5. Phytoplankton Community Stability Across Eutrophic Gradients: Insights From Annual and Monthly Timescales NSTL国家科技图书文献中心

Wang, Liya | Yang, Zhen ... - 《Freshwater Biology》 - 2025,70(2) - 共14页

摘要：. However, it remains unclear how the annual and monthly | monthly (extending to 96 months within the same period | , with monthly* data) scales to examine how they changed* | and monthly* BS was only slightly affected by the* | eutrophication gradient, whereas monthly* composition stability*

关键词： biomass stability | composition stability | eutrophication | phytoplankton | rank-abundance curve change | rank‐abundance curve change

6. Climate Downscaling Monthly Coastal Sea Surface Temperature Using Convolutional Neural Network and Composite Loss NSTL国家科技图书文献中心

Chen Wang | Erik Behrens ... - 《AI 2024: Advances in Artificial Intelligence,Part I》 - Australasian Joint Conference on Artificial Intelligence - 2025, - 303～315 - 共13页

摘要： incorporate historical monthly* mean SST, enhancing its* | GCM data and observational monthly* SST data show our* | Climate downscaling bridges the gap between | coarse-resolution General Circulation Model (GCM | ) outputs and the fine-resolution data needed for regional

关键词： Seasonal climate forecast | Deep learning

7. Monthly High‐Resolution Historical Climate Data for North America Since 1901 NSTL国家科技图书文献中心

Tongli Wang | Andreas Hamann ... - 《International Journal of Climatology》 - 2025,45(3) - n/a～n/a - 共9页

摘要： monthly historical time series grids since 1901 for our | based on interpolations of monthly* anomalies (change* | ABSTRACT Interpolated grids of historical | climate variables are widely used in climate change | impact and adaptation research. Here, we contribute

关键词： climate anomalies | ClimateNA | downscaling | historical climate data | thin plate spline interpolation

8. Monthly mapping of Indonesia’s burned areas: implementation, history, techniques, and future directions NSTL国家科技图书文献中心

Yenni Vetrita | Israr Albar ... - 《International journal of remote sensing》 - 2025,46(1/2) - 636～660 - 共25页

摘要：) the most recent techniques for producing monthly* BA* | reduced the accuracy of the national monthly* BA product* | ABSTRACT Forest and land fires cause | substantial economic, social, and environmental devastation | . Interagency forest and land fire management has succeeded in

关键词： Burned area | remote sensing | fires | Indonesia

9. Unlocking Southern Ocean Under-Ice Seasonality With a New Monthly Climatology NSTL国家科技图书文献中心

Yamazaki, Kaihe | Bindoff, Nathaniel L...... - 《Journal of Geophysical Research,C.Oceans》 - 2025,130(1) - n/a～n/a - 共25页

摘要： monthly climatology of the Southern Ocean (south of 40 | new monthly* climatology of the Southern Ocean and* | The advent of under-ice profiling float and | biologging techniques has enabled year-round observation of | the Southern Ocean and its Antarctic margin. These

关键词： southern ocean | climatology | oceanography | seasonality | sea ice | ocean monitoring

10. The Effect of the North Atlantic Oscillation on Monthly Precipitation in Selected European Locations: A Non-Linear Time Series Approach NSTL国家科技图书文献中心

Changli He | Jian Kang ... - 《Environmetrics》 - 2025,36(2) - e2896.1～e2896.21 - 共21页

摘要： monthly precipitation in 30 European cities and towns | results, based on monthly* time series from 1851 up until* | 2020, include shifting monthly* means for the rainfall* | In this article, the relationship between the | , and two Algerian ones, and the North Atlantic

关键词： changing seasonality | climate change | local stationarity | long precipitation series | non-linear time series | time-varying parameter | vector smooth transition autoregression

摘要： how fine-grained smart meter data and monthly | profile generation is proposed which only uses monthly | Consumer load profiling involves examining | patterns of energy consumption using available data. With | smart meter data available at (sub-)hourly intervals

关键词： Load profile generation | Monthly electricity data | Multi-layer perceptron | Random forest | XGboost

摘要： monthly hydropower generation, this study outlines an | for evaluating monthly hydropower generation to | and quasiperiodic signal identification for monthly | As one classical utility for timeseries | analyzing, the Fourierseries (FS) technique has been

关键词： CBS | engineering education | FS | hydropower

摘要： missing values in the hourly monthly median foF2 | Ionospheric databases, like most time series | of experimental parameters, contain missing data | owing to either natural or technical reasons, limiting | the statistical methods that can be used to analyse

关键词： machine learning | ionosphere | random forest | gradient boosting | missing data

摘要： improve monthly precipitation forecasts by integrating | monthly precipitation forecasts. All models exhibited | enhance monthly precipitation forecasts, providing | This study developed a novel framework to | multi-source data with machine-learning techniques

关键词： Multi-source data integration | Machine learning models | Monthly precipitation forecasting | Global precipitation predictions | Upper Blue Nile Basin

摘要：. However, it remains unclear how the annual and monthly | monthly (extending to 96 months within the same period | , with monthly data) scales to examine how they changed | and monthly BS was only slightly affected by the | eutrophication gradient, whereas monthly composition stability

关键词： biomass stability | composition stability | eutrophication | phytoplankton | rank-abundance curve change | rank‐abundance curve change

摘要： incorporate historical monthly mean SST, enhancing its | GCM data and observational monthly SST data show our | Climate downscaling bridges the gap between | coarse-resolution General Circulation Model (GCM | ) outputs and the fine-resolution data needed for regional

关键词： Seasonal climate forecast | Deep learning

摘要： monthly historical time series grids since 1901 for our | based on interpolations of monthly anomalies (change | ABSTRACT Interpolated grids of historical | climate variables are widely used in climate change | impact and adaptation research. Here, we contribute

关键词： climate anomalies | ClimateNA | downscaling | historical climate data | thin plate spline interpolation

摘要：) the most recent techniques for producing monthly BA | reduced the accuracy of the national monthly BA product | ABSTRACT Forest and land fires cause | substantial economic, social, and environmental devastation | . Interagency forest and land fire management has succeeded in

关键词： Burned area | remote sensing | fires | Indonesia

摘要： monthly climatology of the Southern Ocean (south of 40 | new monthly climatology of the Southern Ocean and | The advent of under-ice profiling float and | biologging techniques has enabled year-round observation of | the Southern Ocean and its Antarctic margin. These

关键词： southern ocean | climatology | oceanography | seasonality | sea ice | ocean monitoring

摘要： monthly precipitation in 30 European cities and towns | results, based on monthly time series from 1851 up until | 2020, include shifting monthly means for the rainfall | In this article, the relationship between the | , and two Algerian ones, and the North Atlantic

关键词： changing seasonality | climate change | local stationarity | long precipitation series | non-linear time series | time-varying parameter | vector smooth transition autoregression

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国家科技图书文献中心

摘要： monthly hydropower generation, this study outlines an | for evaluating monthly* hydropower generation to* | and quasiperiodic signal identification for monthly | As one classical utility for timeseries | analyzing, the Fourierseries (FS) technique has been

摘要： missing values in the hourly monthly* median foF2* | Ionospheric databases, like most time series | of experimental parameters, contain missing data | owing to either natural or technical reasons, limiting | the statistical methods that can be used to analyse

摘要： improve monthly* precipitation forecasts by integrating* | monthly precipitation forecasts. All models exhibited | enhance monthly* precipitation forecasts, providing* | This study developed a novel framework to | multi-source data with machine-learning techniques

摘要： incorporate historical monthly* mean SST, enhancing its* | GCM data and observational monthly* SST data show our* | Climate downscaling bridges the gap between | coarse-resolution General Circulation Model (GCM | ) outputs and the fine-resolution data needed for regional

摘要： monthly historical time series grids since 1901 for our | based on interpolations of monthly* anomalies (change* | ABSTRACT Interpolated grids of historical | climate variables are widely used in climate change | impact and adaptation research. Here, we contribute

摘要：) the most recent techniques for producing monthly* BA* | reduced the accuracy of the national monthly* BA product* | ABSTRACT Forest and land fires cause | substantial economic, social, and environmental devastation | . Interagency forest and land fire management has succeeded in

摘要： monthly climatology of the Southern Ocean (south of 40 | new monthly* climatology of the Southern Ocean and* | The advent of under-ice profiling float and | biologging techniques has enabled year-round observation of | the Southern Ocean and its Antarctic margin. These

摘要： monthly precipitation in 30 European cities and towns | results, based on monthly* time series from 1851 up until* | 2020, include shifting monthly* means for the rainfall* | In this article, the relationship between the | , and two Algerian ones, and the North Atlantic