检索结果-内蒙古大学图书馆

A Kernel Density Estimation Approach and Statistical generalized additive model of Western North Pacific Typhoon Activities

引用

ATMOSPHERE 2022年第7期13卷 1128页

作者： Wang, Xiang Wahiduzzaman, Md Yeasmin, Alea Nanjing Univ Informat Sci & Technol Inst Climate & Applicat Res Nanjing 210044 Peoples R China Federat Univ Sch Engn Informat Technol & Phys Sci Ballarat Vic 3350 Australia

This paper presents a development of a statistical model of typhoon genesis, tracks based on kernel density estimation and a generalized additive model (GAM). modeling of typhoon activity is ultimately beneficial to the people living in coastal zones, insurance/re-insurance companies, policy, planning and decision departments. A 50-year record (1972-2021) of typhoon track observations from the International Best Track Archive Climate Stewardship have been used to observe the distribution of typhoon genesis by kernel density estimation. The tracks are simulated through the development of a GAM. It reproduces the observation well. A distance calculation approach between observed and simulated tracks' landfall have been used to validate the model and the model shows a very good skill (approximately 75%).

关键词： kernel density estimation generalized additive model typhoon Western North Pacific

来源：评论

学校读者我要写书评

暂无评论

Adaptive Load Forecasting Methodology Based on generalized additive model with Automatic Variable Selection

引用

SENSORS 2022年第19期22卷 7247-7247页

作者： Krstonijevic, Sovjetka Univ Belgrade Inst Mihajlo Pupin Belgrade 11000 Serbia Univ Belgrade Sch Elect Engn Belgrade 11000 Serbia

For decentralized energy management in a smart grid, there is a need for electric load forecasting at different places in the grid hierarchy and for different levels of aggregation. Load forecasting functionality relies on the load time series prediction model, which provides accurate forecasts. Complex and heterogeneous multi-source load time series in a smart grid require flexible modeling approaches to meet the accuracy demand. This work proposes an adaptive load forecasting methodology based on the generalized additive model (GAM) with the big data estimation method. It is based on a set of GAM terms, constructed for a specific multi-source load forecasting application in the grid and a procedure that dynamically selects the most relevant terms and generates forecasts for particular load time series. Data from publicly available New York Independent System Operator (NYISO) databases are used for testing. The 24-hour-ahead forecasting results for eleven New York City zones, of different sizes and types, indicate the applicability of the proposed methodology.

关键词： electricity load forecasting generalized additive model automatic variable selection smart grid

来源：评论

学校读者我要写书评

暂无评论

Spatial-temporal generalized additive model for modeling COVID-19 mortality risk in Toronto, Canada

引用

SPATIAL STATISTICS 2022年第0期49卷 100526页

作者： Feng, Cindy Dalhousie Univ Fac Med Dept Community Hlth & Epidemiol Halifax NS B3H 1V7 Canada

This article presents a spatial-temporal generalized additive model for modeling geo-referenced COVID-19 mortality data in Toronto, Canada. A range of factors and spatial-temporal terms are incorporated into the model. The non-linear and interactive effects of the neighborhood-level factors, i.e., population density and average of income, are modeled as a two-dimensional spline smoother. The change of spatial pattern over time is modeled as a three-dimensional tensor product smoother. By fitting this model, the space-time effect can uncover the underlying spatial-temporal pattern that is not explained by the covariates. The performance of the modeling method based on the individual data is also compared to the modeling methods based on the aggregated data in terms of in-sample and out-of-sample predictive checking. The results suggest that the individual-level based analysis provided a better overall model fit and higher predictive accuracy for detecting epidemic peaks in this application as compared to the analysis based on the aggregated data. (C) 2021 Elsevier B.V. All rights reserved.

关键词： COVID-19 mortality generalized additive model Space-time interaction Thin plate spline Tensor product smooth

来源：评论

学校读者我要写书评

暂无评论

Evaluation of Abiotic Controls on Windthrow Disturbance Using a generalized additive model: A Case Study of the Tatra National Park, Slovakia

引用

FORESTS 2020年第12期11卷 1259-1259页

作者： Falt'an, Vladimir Katina, Stanislav Minar, Jozef Polcak, Norbert Banovsky, Martin Maretta, Martin Zamecnik, Stanislav Petrovic, Frantisek Comenius Univ Fac Nat Sci Dept Phys Geog & Geoecol Ilkovicova 6 Bratislava 84215 Slovakia Masaryk Univ Fac Sci Inst Math & Stat Kotlarska 267-2 Brno 61137 Czech Republic Slovak Hydrometeorol Inst Dept Meteorol Forecasts & Warnings Jeseniova 17 Bratislava 83315 Slovakia T MAPY Slovensko Ltd Medeny Hamor 15 Banska Bystrica 97401 Slovakia Esprit Ltd Pletiarska 2 Banska Stiavnica 96901 Slovakia Constantine Philosopher Univ Nitra Fac Nat Sci Dept Ecol & Environmentalist Nitra 94901 Slovakia

Windthrows are the most important type of disturbance occurring in the forests of Central Europe. On 19 November 2004, the strong northeastern katabatic winds caused significant damage and land cover change to more than 126 km(2) of spruce forests in the Tatra National Park. The risk of subsequent soil erosion and accelerated runoff has increased in the affected habitats. Similar situations may reoccur this century as a consequence of climate change. A geographical approach and detailed research of the damaged area with more comprehensive statistical analyses of 47 independent variables will help us to obtain a deeper insight into the problem of windthrow disturbances. The results are based on a detailed investigation of the damaged stands, soil, and topography. A comprehensive input dataset enabled the evaluation of abiotic controls on windthrow disturbance through the use of a generalized additive model (GAM). The GAM revealed causal linear and nonlinear relationships between the local dependent quantitative variables (the damage index and the uprooting index) and independent variables (various soil and topography properties). Our model explains 69% of the deviance of the total damage. The distribution of the wind force depended upon the topographical position-mainly on the distance from the slope's foot lines. The soil properties (mainly the soil skeleton, i.e., rock fragments in stony soils) affect the rate and manner of damage (uprooting), especially on sites with less wind force. Stem breakage with no relation to the soil prevailed in places with high force winds. The largest number of uprooted trees was recorded in localities without a soil skeleton. The spruce's waterlogged shallow root system is significantly prone to uprooting. The comprehensive research found a significant relationship between the abiotic variables and two different measures of forest damage, and can expand the knowledge on wind impact in Central European forests.

关键词： spruce forests wind disturbances climate change abiotic factors generalized additive model biodiversity conservation forest ecosystem management Slovakia

来源：评论

学校读者我要写书评

暂无评论

generalized Sparse additive model with Unknown Link Function 24

Generalized Sparse Additive Model with Unknown Link Function

引用

24th IEEE International Conference on Data Mining, ICDM 2024

作者： Yuan, Peipei You, Xinge Chen, Hong Zhang, Xuelin Peng, Qinmu School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China College of Informatics Huazhong Agricultural University Wuhan China

ISBN: (纸本)9798331506681

generalized additive models (GAMs) have been successfully applied to high dimensional data. However, most existing methods cannot capture the high level feature patterns from complex data. To alleviate this problem, we propose a new sparse additive model, named generalized sparse additive model with unknown link function (GSAMUL), in which the component functions are estimated by B-spline basis and the unknown link function is estimated by a multi-layer perceptron (MLP) network. Furthermore, l2.1 -norm regularizer is used for variable selection. The proposed GSAMUL can realize both variable selection and hidden interaction. We integrate this estimation into a bilevel optimization problem, where the data is split into training set and validation set. In theory, we provide the guarantees about the convergence of the approximate procedure. In applications, experimental evaluations on both synthetic and real world data sets consistently validate the effectiveness of GSAMUL. © 2024 IEEE.

关键词： bilevel optimization convergence analysis generalized additive model unknown link function variable interaction

来源：评论

学校读者我要写书评

暂无评论

On forecasting the intraday Bitcoin price using ensemble of variational mode decomposition and generalized additive model

引用

JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2020年第3期34卷 1003-1009页

作者： Gyamerah, Samuel Asante Pan African Univ Inst Basic Sci Technol & Innovat Lagos Kenya

High frequency Bitcoin price series are often non-linear and non-stationary and hence forecasting the price of Bitcoin directly or by transformation using statistical models is subject to large errors. This paper presents an ensemble model using variational mode decomposition (VMD) and generalized additive model (GAM) to forecast intraday Bitcoin price. To evaluate the performance of the constructed model, it is compared with an ensemble of empirical mode decomposition (EMD) and GAM. The results showed that VMD-GAM model performed better than the EMD-GAM ensemble model in terms of three evaluation metrics (root mean square error, mean absolute percentage error, and bias) used. (c) 2020 The Author. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Bitcoin price forecasting Empirical mode decomposition Variational mode decomposition generalized additive model Ensemble forecast

来源：评论

学校读者我要写书评

暂无评论

Influence of weather and air pollution on concentration change of PM2.5 using a generalized additive model and gradient boosting machine

引用

ATMOSPHERIC ENVIRONMENT 2021年 255卷 118437-118437页

作者： Cheng, Bowen Ma, Yuxia Feng, Fengliu Zhang, Yifan Shen, Jiahui Wang, Hang Guo, Yongtao Cheng, Yifan Lanzhou Univ Coll Atmospher Sci Key Lab Semiarid Climate Change Minist Educ Lanzhou 730000 Peoples R China

Particulate matter with a diameter of <2.5 mu m (PM2.5) is a critical air pollutant that adversely affects human health and the ecological environment. Using the data of daily air pollutant concentration and meteorological elements from 2013 to 2020 in Lanzhou, China, we employed the generalized additive model (GAM) and gradient boosting machine (GBM) approaches to analyze the relationship between PM2.5 concentration and environmental factors. The results revealed that the annual average PM2.5 concentration in Lanzhou, China, decreased by 3.98 mu g/m3 per year during the study period and the variation of PM2.5 concentration was influenced by many factors. Mean temperature, air pressure, relative humidity, and O3 concentration had significantly negative effects on PM2.5 concentration, whereas CO and NO2 concentrations had significant positive effects. PM2.5 concentration was also high during static winds or high-speed winds. Among these influencing factors, the interactions of meteorological factors and air pollution also had strong correlations with PM2.5 concentration. We found that the multi-factor model could better explain the influence of environmental factors on PM2.5 concentration than the single-factor model. The fitting degree of the GBM was better than that of the GAM model;their mean absolute errors were 11.30 and 11.85, respectively. We also found that the GBM model was more precise for estimating daily PM2.5 concentration, whereas the GAM was more suitable for long-term trend analysis.

关键词： PM2 5 Meteorological factors Air pollution generalized additive model Gradient boosting machine

来源：评论

学校读者我要写书评

暂无评论

Combined generalized additive model and Random Forest to evaluate the influence of environmental factors on phytoplankton biomass in a large eutrophic lake

引用

ECOLOGICAL INDICATORS 2021年 130卷 108082-108082页

作者： Zhang, Jinpeng Zhi, Mengmeng Zhang, Ying Shandong Normal Univ Coll Geog & Environm Jinan 250358 Shandong Peoples R China Chinese Res Inst Environm Sci Beijing 100012 Peoples R China Xian Univ Architecture & Technol Sch Environm & Municipal Engn Xian 710055 Shaanxi Peoples R China

The over-proliferation of phytoplankton has been a public concern for the last several decades. To evaluate the importance of different environmental factors on phytoplankton biomass variations with their complex response relationships in the large eutrophic Lake Okeechobee, the nonlinear methods including generalized additive model (GAM) and Random Forest (RF) algorithm were employed. A long-term monitoring dataset of 8 sampling sites ranging from January 1996 to December 2010 was applied to explore the driven factors and response relationships of phytoplankton biomass from the different scales in Lake Okeechobee. Results showed a spatially heterogeneous distribution of phytoplankton biomass there, and the western and southern littoral zones occurred heavier algal blooms than the central pelagic zones. Spearman's correlation results denoted Chlorophyll a (Chla) was negatively correlated with phosphorus in different scales. In the aspect of temporal variations, phytoplankton biomass appeared a three-peak-two-valley variation trend there, which peaked in 1997, 2003, and 2010, respectively. On the lake-wide scale, the RF model indicated that the inorganic nutrients were the primary predictors of phytoplankton biomass, while underwater light availability factors were followed to play essential roles in the prediction. On the local scale, total nitrogen (TN) was the top predictor in the western near-shore zone, while other zones were nitrate and nitrite (NO23) or phosphate (PO4). GAM results suggested that phytoplankton biomass had positive response to TN increase in both lake-wide and local scales. However, its response to water temperature (WT) appeared spatial heterogeneity. This study provided a new perspective to evaluate the primary predictors of phytoplankton biomass with their response relationships from lake-wide scale and local scale in the large shallow eutrophic lake, and the importance of taking spatial heterogeneity into account for lake water quality mana

关键词： Eutrophication Lake Okeechobee Random Forest Nutrients generalized additive model

来源：评论

学校读者我要写书评

暂无评论

Source attribution of air pollution using a generalized additive model and particle trajectory clusters

引用

SCIENCE OF THE TOTAL ENVIRONMENT 2021年 780卷 146458-146458页

作者： de Foy, Benjamin Heo, Jongbae Kang, Ji-Yoon Kim, Hwajin Schauer, James J. St Louis Univ Dept Earth & Atmospher Sci St Louis MO 63103 USA Busan Dev Inst Busan South Korea Korea Inst Sci & Technol Ctr Environm Hlth & Welf Res Seoul South Korea Univ Wisconsin Environm Chem & Technol Program Madison WI USA Wisconsin State Lab Hyg Madison WI USA

Speciated hourly measurements of fine aerosols were made for more than two years at an urban, an industrial and a port site in Busan, Korea. A generalized additive model (GAM) was designed to deconvolve factors contributing to the pollutant concentrations at multiple scales. The model yields estimates of source contributions to pollution by separately identifying the signals in the time series due to meteorology, vertical mixing, horizontal wind transport and temporal variations such as diurnal, weekly, seasonal and annual trends. The GAM model was expanded to include FLEXPART back trajectory clusters generated using fuzzy c-means clustering. This made it possible to quantify the impact of long-range transport using the Trajectory Cluster Contribution Function (TCCF). TCCF provides a development of methods such as Concentration Field Analysis and Potential Source Contribution Function by providing numerical estimates of concentration changes associated with different air mass transport patterns while accounting for possible confounding factors from meteorology. The GAM simulations identified the importance of local transport for primary pollutants and long-range transport from China for secondary pollutants. Local factors accounted for up to 72% of the variance in concentrations of NO2 and elemental carbon whereas large-scale/seasonal factors accounted for up to 56% of PM2.5 and 80% of inorganic species. The algorithm further identified the importance of the weekend effect and the holiday effect at the different sites in Busan. The residual from the analysis was used to estimate the impact of the COVID-19 pandemic. The signature of the pandemic was different between the pollutants as well as from site to site. The model was able to distinguish small impacts from local pollutants at the residential site;short-lived acute impacts from industrial changes;and longer-term changes due to the early pandemic response in China. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Aeorosols Chemical speciation Lockdown effect Weekend effect generalized additive model Particle trajectories

来源：评论

学校读者我要写书评

暂无评论

A generalized additive model-based data-driven solution for lithium-ion battery capacity prediction and local effects analysis

引用

TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2021年

作者： Chen, Tao Gao, Ciwei Hui, Hongxun Cui, Qiushi Long, Huan Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Univ Macau State Key Lab Internet Things Smart City Zhuhai Peoples R China Arizona State Univ Dept Elect & Comp Engn Tempe AZ 85287 USA

Lithium-ion battery-based energy storage systems have been widely utilized in many applications such as transportation electrification and smart grids. As a key health status indicator, battery performance would highly rely on its capacity, which is easily influenced by various electrode formulation parameters within a battery. Due to the strongly coupled electrical, chemical, thermal dynamics, predicting battery capacity, and analysing the local effects of interested parameters within battery is significantly important but challenging. This article proposes an effective data-driven method to achieve effective battery capacity prediction, as well as local effects analysis. The solution is derived by using generalized additive models (GAM) with different interaction terms. Comparison study illustrate that the proposed GAM-based solution is capable of not only performing satisfactory battery capacity predictions but also quantifying the local effects of five important battery electrode formulation parameters as well as their interaction terms. Due to data-driven nature and explainability, the proposed method could benefit battery capacity prediction in an efficient manner and facilitate battery control for many other energy storage system applications.

关键词： Lithium-ion battery battery capacity prediction generalized additive model data-driven solution energy storage system

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：