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Evaluating land ecological security and examining its relationships with driving factors using GIS and generalized additive model

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SCIENCE OF THE TOTAL ENVIRONMENT 2018年 633卷 1469-1479页

作者： Feng, Yongjiu Yang, Qianqian Tong, Xiaohua Chen, Lijuan Shanghai Ocean Univ Coll Marine Sci Shanghai 201306 Peoples R China Shanghai Ocean Univ Key Lab Sustainable Exploitat Ocean Fisheries Res Shanghai 201306 Peoples R China Univ Queensland Sch Earth & Environm Sci Brisbane Qld 4072 Australia Tongji Univ Coll Surveying & Geoinformat Shanghai 200092 Peoples R China Chinese Acad Sci Key Lab Ecohydrol Inland River Basin Northwest Inst Ecoenvironm & Resources Lanzhou 730000 Gansu Peoples R China

Land ecological security (LES) refers to the environmental health and sustainability of the land resources and ecosystems, which are substantially affected by biophysical and socio-economic factors. We assess the spatiotemporal patterns of LES in Ningbo city on the southeast coast of China from 1975 to 2015 and explore the effects of driving factors. Expert evaluation is used to estimate the LES score for each 2 x 2 km grid and map the patterns by Kriging. Five levels of LES are used: very secure, secure, neutral, insecure and very insecure. A generalized additive model (GAM) captures the relationships between LES and driving factors, and identifies the dominant factors. Our results show that the Ningbo LES has been deteriorating since 1975, and it is now very insecure in Ningbo city center and the central area of the satellite city Cixi. The dominant factors affecting LES are distances to city center (D-city), district center (D-district) and road networks (D-roads), and the moving window built-up area (D-ndbi). Among these, D-ndbi is most important as inferred by the highest explained deviance of the GAM. This study improves our understanding of the spatiotemporal patterns of LES in Ningbo and how LES changes. As a result, it provides insight to help local governments optimize land-use configuration, potentially improving the environment and ecosystems and creating a more environmentally friendly city. (c) 2018 Elsevier B.V. All rights reserved.

关键词： Land ecological security Spatial patterns Spatial determinants generalized additive model Ningbo

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Air pollution and coronary heart disease-related hospital visits in Beijing, China: time-series analysis using a generalized additive model

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第13期30卷 36938-36951页

作者： Gao, Yuan Sheng, Weixuan Yang, Yongtao Capital Med Univ Beijing Shijitan Hosp Dept Anesthesiol Beijing 100038 Peoples R China Tsinghua Univ Beijing Huaxin Hosp Affiliated Hosp 1 Dept Blood Transfus Beijing 100016 Peoples R China

To investigate correlations between environmental and meteorological factors and frequency of presentation for coronary heart disease (CHD) in Beijing. Daily measurements of levels of six atmospheric pollutants were made, data relating to meteorological conditions collected, and CHD-related outpatient visits recorded from January 2015 to December 2019 in Beijing. A time-series analysis was made, using a generalized additive model with Poisson distribution, and R 3.6.3 software was used to estimate relationships among levels of atmospheric pollutants, ambient temperature, and visits occasioned by CHD. Results were controlled for time-dependent trend, other weather variables, day of the week, and holiday effects. Lag-response curves were plotted for specific and incremental cumulative effects of relative risk (RR). The aim was to correlate meteorological-environmental factors and the daily number of CHD-related hospital visits and to quantify the degree of correlation to identify any pathological associations. Response diagrams and three-dimensional diagrams of predicted exposure lag effects were constructed in order to evaluate relationships among the parameters of air pollution, temperature, and daily CHD visits. The fitted model was employed to predict the lag RR and 95% confidence interval (95% CI) for specific and incremental cumulative effects of random air pollutants at random concentrations. This model may then be used to predict effects on the outcome variable at any concentration of any defined pollutant, giving flexibility for public health purposes. The overall lag-response RR curves for the specific cumulative effects of the pollutants, particulate matter (PM)2.5, PM10, SO2, CO, and NO2, were statistically significant and for PM2.5, PM10, CO, and NO2, the overall lag-response RR curves for the incremental cumulative effect were statistically significant. When PM2.5, PM10, SO2, CO, and NO2 concentrations were above threshold values and the temperature was

关键词： Coronary heart disease Environmental-meteorological factors generalized additive model Time-series analysis

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

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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

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Regression and generalized additive model to Enhance the Performance of Photovoltaic Power Ensemble Predictors

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IEEE ACCESS 2021年 9卷 111899-111914页

作者： Sundararajan, Aditya Ollis, Ben Oak Ridge Natl Lab Grid Components & Controls Grp Oak Ridge TN 37932 USA

Photovoltaic (PV) power prediction has a constantly evolving solutions landscape with a myriad of data-driven techniques. Each technique leverages a self-adaptive algorithm that must retrain in intervals, be it each day, week, or season, to avoid the model generalizing poorly because of overfitting, underfitting, or concept drift. This paper aims to improve the generalization capability of PV power predictors such as autoencoders used widely in the industry by introducing feature-enhanced ensemble learning (FEEL) after the feature selection step. This framework uses a combination of nonparametric regression and generalized additive models, and an ensemble of weak regularized multilayer perceptron models. Once trained, the framework can reliably generalize on test data across long time periods without any significant degradation in performance. The proposed framework was validated against the baseline autoencoder-based feature enhancement model on a real PV system from a smart neighborhood in Alabama for September 2019. The FEEL framework performed three times better than the baseline, but when applied to the baseline, its performance improved by two times on average. Furthermore, the framework generalized consistently better than five other feature enhancement strategies. Despite fluctuations in weather, the FEEL framework's R-square score had a range of 8.1%, whereas that of the baseline was 48.3%. The mutual information and Minkowski distance scores attempted to quantify concept and model drift, respectively. These scores show that the FEEL framework generalized the ensemble learning models at least two times better than the baseline across the different test days. These results form the first step toward decentralized intelligence for smart grid applications that could free up resources for other expensive analytics in the field.

关键词： Predictive models Photovoltaic systems Government Neural networks Data models Licenses Forecasting Feature enhancement nonparametric regression ensemble learning generalized additive model concept drift photovoltaic power prediction

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A time-series analysis on generalized additive model for atmospheric pollen concentration and the number of visits of allergic conjunctivitis, Beijing, China

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2022年第40期29卷 61522-61533页

作者： Sheng, Weixuan Liu, Aizhu Peng, Hong Wang, Jia Guan, Lei Capital Med Univ Dept Anesthesiol Affiliated Beijing Shijitan Hosp 10 Yangfangdian Railway Hosp Rd Beijing 100038 Peoples R China Capital Med Univ Dept Otolaryngol Head & Neck Surg Affiliated Beijing Shijitan Hosp Beijing Peoples R China

The objective of this study is to investigate the correlation between atmospheric pollen concentration and daily visits for allergic conjunctivitis. Daily counts of outpatient visits for allergic conjunctivitis, atmospheric pollen concentration, and meteorological data during pollen season of 2018 and 2019 were collected from Beijing Shijitan Hospital, China. A time-series analysis on generalized additive model with Poisson distribution was used to estimate the relationship between pollen concentration and visits for allergic conjunctivitis, after controlling for the time trend, weather variables, day of the week, and holiday effect. The RStudio was used to generate Spearman correlation coefficients and then to plot the lag-response curves for specific and incremental cumulative effects of relative risk (RR). There was a moderate positive correlation between pollen concentration and visits for allergic conjunctivitis, and Spearman's correlation coefficient was 0.521 in 2018 and 0.515 in 2019 (P<0.01). The specific cumulative effect peak associated with per 10 grains/kmm(2) increases of atmospheric pollen concentration was within 0 day, and the lag time was 8 days(2018, 2019). The incremental cumulative effect peak associated with per 10 grains/kmm(2) increases of atmospheric pollen concentration occurred on lag day 10 (2018) and lag day 8 (2019), and the lag time was 14 days (2018) and 20 days (2019). Elevated concentrations of pollen increase the risk of allergic conjunctivitis with a time lag effect.

关键词： Allergic conjunctivitis Pollen generalized additive model Time-series analysis

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North American Breeding Bird Survey status and trend estimates to inform a wide range of conservation needs, using a flexible Bayesian hierarchical generalized additive model

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ORNITHOLOGICAL APPLICATIONS 2021年第1期123卷 1页

作者： Smith, Adam Edwards, Brandon Canadian Wildlife Serv Environm Climate Change Canada Natl Wildlife Res Ctr Ottawa ON Canada Univ Guelph Dept Math & Stat Guelph ON Canada

The status and trend estimates derived from the North American Breeding Bird Survey (BBS) are critical sources of information for bird conservation. However, the estimates are partly dependent on the statistical model used. Therefore, multiple models are useful because not all of the varied uses of these estimates (e.g., inferences about long-term change, annual fluctuations, population cycles, and recovery of once-declining populations) are supported equally well by a single statistical model. Here we describe Bayesian hierarchical generalized additive models (GAMs) for the BBS, which share information on the pattern of population change across a species' range. We demonstrate the models and their benefits using data from a selection of species, and we run full cross-validation of the GAMs against 2 other models to compare the predictive fit. The GAMs have a better predictive fit than the standard model for all species studied here and comparable predictive fit to an alternative first difference model. In addition, one version of the GAM described here (GAMYE) estimates a population trajectory that can be decomposed into a smooth component and the annual fluctuations around that smooth component. This decomposition allows trend estimates based only on the smooth component, which are more stable between years and are therefore particularly useful for trend-based status assessments, such as those by the International Union for the Conservation of Nature. It also allows for the easy customization of the model to incorporate covariates that influence the smooth component separately from those that influence annual fluctuations (e.g., climate cycles vs. annual precipitation). For these reasons and more, this GAMYE model is a particularly useful model for the BBS-based status and trend estimates.

关键词： Bayesian Breeding Bird Survey cross-validation generalized additive model population change status and trend estimates

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A generalized additive model for Binary Rare Events Data: An Application to Credit Defaults 1

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Joint International Meeting of the Japanese-Classification-Society and the Classification-and-Data-Analysis-Group of the Italian-Statistical-Society (JCS-CLADAG)

作者： Calabrese, Raffaella Osmetti, Silvia Angela Univ Essex Essex Business Sch Colchester CO4 3SQ Essex England Univ Cattolica Sacro Cuore Largo Gemelli 1 I-20123 Milan Italy

ISBN: (数字)9783319066929

ISBN: (纸本)9783319066929;9783319066912

We aim at proposing a new model for binary rare events, i.e. binary dependent variable with a very small number of ones. We extend the generalized Extreme Value (GEV) regression model proposed by Calabrese and Osmetti (Journal of Applied Statistics 40(6): 1172-1188, 2013) to a generalized additive model (GAM). We suggest to consider the quantile function of the GEV distribution as a link function in a GAM, so we propose the generalized Extreme Value additive (GEVA) model. In order to estimate the GEVA model, a modified version of the local scoring algorithm of GAM is proposed. Finally, to model default probability, we apply our proposal to empirical data on Italian Small and Medium Enterprises (SMEs). The results show that the GEVA model has a higher predictive accuracy to identify the rare event than the logistic additive model.

关键词： generalized additive model generalized extreme value distribution Rare event

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Comparison of logbook reports of incidental blue shark catch rates by Hawaii-based longline vessels to fishery observer data by application of a generalized additive model

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FISHERIES RESEARCH 2002年第1期58卷 79-94页

作者： Walsh, WA Kleiber, P McCracken, M Univ Hawaii Joint Inst Marine & Atmospher Res Honolulu HI 96822 USA

A generalized additive model (GAM) of blue shark, Prionace glauca, catch rates (catch per set) was fitted to data gathered by National Marine Fisheries Service (NMFS) observers stationed aboard Hawaii-based commercial longline vessels (N = 2010 longline sets) from March 1994 to December 1997. Its coefficients were then applied to the values of predictor variables, which were also contained in logbook records that described the remainder of fishery-wide effort during the study period (N = 41319 longline sets). The objective was to determine whether predictions generated by such a GAM could serve in lieu of observers on the large fraction of longline trips that do not carry an observer (approximately 95%). After deleting data considered false or inaccurate, much of which was associated with a small number of vessels, the relationship between catch rates as reported in logbooks and GAM predictions was expressed by log(e) (Y + 1) = 0.7952 log(e) (X + 1) - 0.0586 where Y is the catch rate (i.e., the number of blue shark caught per set) and X the GAM predictions (R-2 = 0.307, N = 40 243). Patterns of correspondence between logbook trends and GAM predictions were further refined by plotting the trends according to the type of fishing effort (e.g., tuna- or swordfish-directed). The highest mean catch rates reported in logbooks, the highest mean GAM predictions, and the greatest differences between the two occurred consistently in mid-year on swordfish trips. In contrast, mean values from logbooks and mean GAM predictions were closest for tuna-directed effort, but this reflected an order of magnitude reduction in the scale of catch rates rather than closely similar trends. A bootstrapping algorithm developed for the GAM yielded an estimate of 23.9% under-reporting for the study period, with approximate 95% prediction limits of 15.4-28.9%. We conclude that prediction with a GAM fitted to fishery observer data is a useful monitoring technique for the Hawaii-based commercial lo

关键词： blue shark generalized additive model incidental catch rates fishery observer data logbook reliability

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Ultrahigh-dimensional generalized additive model: Unified theory and methods

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SCANDINAVIAN JOURNAL OF STATISTICS 2022年第3期49卷 917-942页

作者： Yang, Kaixu Maiti, Tapabrata Michigan State Univ Dept Stat & Probabil 619 Red Cedar RdRoom 507 E Lansing MI 48824 USA

generalized additive model is a powerful statistical learning and predictive modeling tool that has been applied in a wide range of applications. The need of high-dimensional additive modeling is eminent in the context of dealing with high throughput data such as genetics data analysis. In this article, we studied a two-step selection and estimation method for ultrahigh-dimensional generalized additive models. The first step applies group lasso on the expanded bases of the functions. With high probability this selects all nonzero functions without having too much over selection. The second step uses adaptive group lasso with any initial estimators, including the group lasso estimator, that satisfies some regular conditions. The adaptive group lasso estimator is shown to be selection consistent with improved convergence rates. Tuning parameter selection is also discussed and shown to select the true model consistently under generalized information criterion procedure. The theoretical properties are supported by extensive numerical study.

关键词： adaptive group lasso generalized additive model high-dimensional variable selection selection consistency tuning parameter selection

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Prediction of interface of geological formations using generalized additive model

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GEORISK-ASSESSMENT AND MANAGEMENT OF RISK FOR ENGINEERED SYSTEMS AND GEOHAZARDS 2022年第1期16卷 127-139页

作者： Qi, Xiaohui Yang, Zhiyong Chu, Jian Northumbria Univ Dept Mech & Construct Engn Newcastle Upon Tyne Tyne & Wear England Sun Yat Sen Univ Sch Civil Engn Guangzhou Peoples R China Nanyang Technol Univ Sch Civil & Environm Engn Singapore Singapore

Geological information such as geological interfaces is important for the design of underground excavation and supporting measures. This in turn requires a method to predict accurately the locations of geological interfaces for the gap areas between boreholes. This study presents a generalized additive model (GAM) to predict the location of the geological interfaces. The performance of the GAM method is evaluated using both simulated data and borehole data for the determination of rockhead in two different geological formations in Singapore. The results show that the GAM method can provide a reasonable confidence interval (CI) of the mean trend and the prediction interval (PI) in the sense that the 95% CI covers about 95% of the actual mean curve while the 95% PI covers around 95% of testing data. Furthermore, the geological complexity can be well reflected as the prediction uncertainty in the geologically complex area is larger than that in the geologically regular area. More importantly, the users can impose prior information or personal judgment regarding the shape of the geological profile on the model. This is an important feature to enable further improvement in the accuracy of the prediction.

关键词： Spatial prediction geological interface generalized additive model cubic spline

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