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Mapping soil organic carbon density via geographically weighted regression with smooth terms: A case study in Shanxi Province

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ECOLOGICAL INDICATORS 2024年 166卷

作者： Zheng, Yutong Zhao, Xiaonan Li, Xiangyu Chen, Hongyu Li, Changcheng Zhang, Chutian Northwest A&F Univ Coll Nat Resources & Environm Yangling 712100 Peoples R China

Soil, as the largest organic carbon reservoir in terrestrial ecosystems, plays a crucial role in the global carbon cycle. Accurately predicting the spatial distribution of soil organic carbon density (SOCD) is highly important for estimating organic carbon stocks and guiding agricultural production. Due to the complex relationship between the spatial distribution of SOCD and environmental factors, its relationship with a specific region is not exclusively linear or nonlinear;both types of relationships may coexist simultaneously. In this study, a new model, geographically weighted regression with smooth terms (SGWR), was proposed. This model can simultaneously consider both global spatial nonlinear relationships and local spatial nonstationary relationships. It was utilized to predict SOCD in Shanxi Province and was compared to multiple linear regression (MLR), generalized additive models (GAM) and geographically weighted regression (GWR) in terms of spatial prediction performance. The results showed that SGWR was highly competitive in predicting the spatial distribution of SOCD in Shanxi Province. Of factors influencing SOCD in Shanxi Province, elevation and slope were considered local linear variables, while the normalized difference vegetation index (NDVI), gross primary productivity (GPP), annual precipitation (AP), and soil moisture (SM) were considered global nonlinear variables. Among these factors, the NDVI and GPP had the most significant impacts on the distribution of SOCD, while elevation and SM had the least significant impacts on it. The results imply that integrating global linear, local linear, and global nonlinear relationships concurrently, as implemented in SGWR, improves predictive accuracy compared to approaches that solely address either local or global linear relationships based on environmental variable changes in SGWR. The results of this study indicate that the SGWR model is potentially a competitive method for digital soil mapping.

关键词： Soil organic carbon density Nonlinear relationship Spatial nonstationary relationship generalized additive models Geographically weighted regression model with smooth terms

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Challenging the Performance-Interpretability Trade-Off: An Evaluation of Interpretable Machine Learning models

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BUSINESS & INFORMATION SYSTEMS ENGINEERING 2025年 1-25页

作者： Kruschel, Sven Hambauer, Nico Weinzierl, Sven Zilker, Sandra Kraus, Mathias Zschech, Patrick Univ Regensburg Chair Explainable AI Business Value Creat Bajuwarenstr 4 D-93053 Regensburg Germany Friedrich Alexander Univ Erlangen Nurnberg Chair Digital Ind Serv Syst Further Str 248 D-90429 Nurnberg Germany TH Nurnberg Georg Simon Ohm Professorship Business Analyt Hohfederstr 40 D-90489 Nurnberg Germany Univ Leipzig Professorship Intelligent Informat Syst & Proc Grimma Str 12 D-04109 Leipzig Germany

Machine learning is permeating every conceivable domain to promote data-driven decision support. The focus is often on advanced black-box models due to their assumed performance advantages, whereas interpretable models are often associated with inferior predictive qualities. More recently, however, a new generation of generalized additive models (GAMs) has been proposed that offer promising properties for capturing complex, non-linear patterns while remaining fully interpretable. To uncover the merits and limitations of these models, the study examines the predictive performance of seven different GAMs in comparison to seven commonly used machine learning models based on a collection of twenty tabular benchmark datasets. To ensure a fair and robust model comparison, an extensive hyperparameter search combined with cross-validation was performed, resulting in 68,500 model runs. In addition, this study qualitatively examines the visual output of the models to assess their level of interpretability. Based on these results, the paper dispels the misconception that only black-box models can achieve high accuracy by demonstrating that there is no strict trade-off between predictive performance and model interpretability for tabular data. Furthermore, the paper discusses the importance of GAMs as powerful interpretable models for the field of information systems and derives implications for future work from a socio-technical perspective.

关键词： Decision support Predictive analytics Interpretable machine learning generalized additive models Explainable artificial intelligence

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Temperature extremes and human health in Cyprus: Investigating the impact of heat and cold waves

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ENVIRONMENT INTERNATIONAL 2025年 199卷

作者： Kekkou, Fragkeskos Economou, Theo Lazoglou, Georgia Anagnostopoulou, Christina Cyprus Inst Climate & Atmosphere Res Ctr CARE C CY-2121 Aglantzia Cyprus Univ Exeter Dept Math & Stat Exeter England Aristotle Univ Thessaloniki Fac Sci Sch Geol Dept Meteorol & Climatol Thessaloniki 54124 Greece

In Mediterranean countries like Cyprus, extreme temperatures and especially heatwaves during summer months are increasingly common, leading to heightened bio-climatic stress. While coldwaves are less frequent and understudied in this region, they still pose significant health risks. This study utilizes temperature data from ERA5-Land in conjunction with mortality and hospitalization data from Cyprus. Employing statistical machine learning methods such as Distributed Lag models (DLMs) and generalized additive models (GAMs), mortality and hospitalization risks for major health concerns in Cyprus, along with attributable deaths and hospital admissions associated with temperature fluctuations and extreme events over two seasons, were estimated. This study also examines temperature trends over the past four decades in Cyprus, noting a significant increase in both maximum and minimum temperatures as well as an increase in extreme high-temperature days and a decrease in extreme low-temperature days. For mortality, elevated risks were identified at very high temperatures, peaking at shorter lags with up to a 7.5% above-average risk, which further increased to 14.3% during heatwave events in the warm season. Conversely, mortality risks at very low temperatures during the cold season reached 4.2% particularly for longer lag days. Hospitalization risks were notably higher during periods of cold temperatures. Importantly, our findings confirm that cold-related mortality exceeds heat-related mortality in absolute terms. However, during heatwave days, the rate of increase in attributable deaths is five times higher compared to typical days in the warm season, whereas for cold days it is about twice as high as on non-cold days.

关键词： Heatwave Coldwave generalized additive models Mortality Hospitalizations Cause-specific Wave-synergy

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Associations Between Anthropogenic Factors, Meteorological Factors, and Cause-Specific Emergency Department Admissions

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GEOHEALTH 2024年第9期8卷 e2024GH001061页

作者： Tewari, Pranav Xu, Baihui Pei, Ma Tan, Kelvin Bryan Abisheganaden, John Yim, Steve Hung-Lam Dickens, Borame Lee Lim, Jue Tao Nanyang Technol Univ Lee Kong Chian Sch Med Singapore Singapore Natl Univ Singapore Saw Swee Hock Sch Publ Hlth Singapore Singapore Minist Hlth Singapore Singapore Tan Tock Seng Hosp Singapore Singapore Nanyang Technol Univ Asian Sch Environm Singapore Singapore

Unpredictable emergency department (ED) admissions challenge healthcare systems, causing resource allocation inefficiencies. This study analyses associations between air pollutants, meteorological factors, and 2,655,861 cause-specific ED admissions from 2014 to 2018 across 12 categories. generalized additive models were used to assess non-linear associations for each exposure, yielding Incidence Rate Ratios (IRR), while the population attributable fraction (PAF) calculated each exposure's contribution to cause-specific ED admissions. IRRs revealed increased risks of ED admissions for respiratory infections (IRR: 1.06, 95% CI: 1.01-1.11) and infectious and parasitic diseases (IRR: 1.09, 95% CI: 1.03-1.15) during increased rainfall (13.21-16.97 mm). Wind speeds >12.73 km/hr corresponded to increased risks of ED admissions for respiratory infections (IRR: 1.12, 95% CI: 1.03-1.21) and oral diseases (IRR: 1.58, 95% CI: 1.31-1.91). Higher concentrations of air pollutants were associated with elevated risks of cardiovascular disease (IRR: 1.16, 95% CI: 1.05-1.27 for PM10) and respiratory infection-related ED admissions (IRR: 2.78, 95% CI: 1.69-4.56 for CO). Wind speeds >12.5 km/hr were predicted to contribute toward 10% of respiratory infection ED admissions, while mean temperatures >28 degrees C corresponded to increases in the PAF up to 5% for genitourinary disorders and digestive diseases. PM10 concentrations >60 mu g/m(3) were highly attributable toward cardiovascular disease (PAF: 10%), digestive disease (PAF: 15%) and musculoskeletal disease (PAF: 10%) ED admissions. CO concentrations >0.6 ppm were highly attributable to respiratory infections (PAF: 20%) and diabetes mellitus (PAF: 20%) ED admissions. This study underscores protective effects of meteorological variables and deleterious impacts of air pollutant exposures across the ED admission categories considered. Plain Language Summary The unpredictability and variability of emergency department (ED) admissions lo

关键词： non-linear associations generalized additive models population attributable fraction Singapore emergency department admissions

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Single-species quotas drive discards by multi-species trawlers in the Celtic Seas ecoregion when their relative abundance fluctuates

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ICES JOURNAL OF MARINE SCIENCE 2024年第9期81卷 1745-1763页

作者： Browne, Maria Calderwood, Julia Brophy, Deirdre Minto, Coilin Atlantic Technol Univ Marine & Freshwater Res Ctr Dublin Rd Galway H91T8NW Ireland

Our study investigated discarding patterns by bottom otter trawlers over 17 years prior to the full implementation of the Landing Obligation. A generalized additive modelling approach was applied to at-sea sampling data to examine the most important drivers of the discards per unit effort (DPUE) of cod, whiting, hake, and haddock in the Celtic Seas ecoregion from 2002 to 2018. Many operational, environmental, regulatory, and spatio-temporal factors were tested. Fish above and below the minimum conservation reference size (MCRS) were modelled separately to compare drivers of discarding of these categories of unwanted catch. In the full time period analysis, the three most important factors in determining DPUE were geographic location, an interaction between geographic location and year, and year interacting with month. Further analysis of a subset of the time series indicated that quota availability significantly impacted the DPUE for MCRS hake, while the quota allocated for one species often significantly influenced the DPUE of other species and size classes. Quota interacting with abundance had a significant impact for all >MCRS species and < MCR Shake. This indicates that changes in relative abundance compound discarding under quota constraints. Collectively, these results empirically demonstrate that single-species quotas majorly contribute to discards in a mixed-fisheries context .

关键词： fishing behaviour generalized additive models landing obligation management measures mixed fisheries unwanted catch

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Using dynamic Bayesian belief networks to infer the effects of climate change and human activities on changes in regional ecosystem services

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ECOLOGICAL INDICATORS 2025年 170卷

作者： Yu, Haoran Jiang, Junyi Gu, Xinchen Cao, Chunwu Shen, Cheng Nanjing Forestry Univ Sch Landscape Architecture Nanjing 210037 Peoples R China China Geol Survey Haikou Marine Geol Survey Ctr Haikou 571127 Peoples R China Tianjin Univ Sch Civil Engn Tianjin 300072 Peoples R China Shanghai Acad Landscape Architecture Sci & Plannin Key Lab Natl Forestry & Grassland Adm Ecol Landscaping Cha Shanghai 200232 Peoples R China

In the context of the Anthropocene, the unparalleled degradation of ecosystem services (ES) has been driven by the intricate nature of ecosystems and their responsive interplay with human actions. This underscores the paramount importance of forecasting and managing these services for regional sustainability. However, a comprehensive understanding of the correlated impacts and driving factors of the spatial patterns of ES remains elusive. In this study, we utilize the dynamic Bayesian belief network (DBN) to infer the probabilistic changes in ecosystem services (biodiversity conservation, carbon storage, soil retention, water regulation, net primary productivity, and crop production) under the influence of climate change and human activities in the Yangtze River Delta urban agglomeration. Through this investigation, we discern conspicuous discrepancies in the impacts of climate and human activities on various ecosystem services and their spatial differentiations in the Yangtze River Delta urban agglomeration. The dynamic Bayesian belief network model reveals that geographical factors and land use play pivotal roles in shaping the spatial patterns of ecosystem services. Concerning spatial differentiation, Water yield (WY) and Biodiversity maintenance (BM) exhibit heightened sensitivity to changes in precipitation, while Net primary productivity (NPP) is significantly influenced by variations in vegetation cover and temperature. Moreover, land use and land cover, as reflections of human activities, greatly affect Carbon storage (CF) through the expansion of construction land and the loss of forested areas. Soil retention (SR), on the other hand, is predominantly influenced by rainfall. Spatial variations in Crop production (CP) are found to be contingent on the extent of vegetation cover. In summary, our findings unveil the non-linear relationships among ecosystem services and their direct and indirect responses to climate change and human activities. These insights h

关键词： Ecosystem service management Dynamic Bayesian models generalized additive models Human activities Climate change

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Spatial patterns in chlorophyll a concentration during the winter-spring - spring periods in the Barents Sea

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PROGRESS IN OCEANOGRAPHY 2024年 228卷

作者： Dvoretsky, Vladimir G. Vodopianova, Veronika V. Bulavina, Aleksandra S. Pastukhov, Ivan A. Russian Acad Sci MMBI RAS Murmansk Marine Biol Inst Moscow Russia

Climatic fluctuations have been documented to strongly affect Arctic marine ecosystems. Plankton assemblages serve as the most sensitive indicators of such environmental forcing. We conducted a study to investigate the spatial variability of chlorophyll a (Chl-a) concentration during two pre-bloom periods (March-April 2021 and February-March 2022) in relation to the distribution of different water masses and associated properties. The upper 50 m layer of the water column was homogeneous and stable, characterized by high nutrient concentrations. Our mapping of the Barents Sea based on Chl-a concentrations revealed low estimates during the winter period. In contrast, two distinct Chl-a peaks were observed in the spring. The first region with high Chl-a concentrations was identified in Murmansk Coastal Water and Atlantic Water (0.7-1.4 mg m-- 3 ), reflecting the positive impact of the frontal zone between these interacting water masses. The second region with elevated Chla concentrations (0.9-1.1 mg m- 3 ) was located in Kolguev-Pechora Water near the southeastern ice edge. Cold water regions (Barents Sea Water, Arctic Water, Novaya Zemlya Coastal Water) exhibited low spring Chl-a concentrations (0.03-0.3 mg m-3).-3 ). generalized additive models identified hydrological variables (temperature and salinity), dissolved oxygen content, and nutrient concentrations (nitrite, nitrate, phosphate) as significant predictors explaining a substantial portion of the Chl-a variability.

关键词： Phytoplankton Chlorophyll a Nutrients Arctic shelf Barents Sea generalized additive models

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Grouping tree species to estimate basal area increment in temperate multispecies forests in Durango,Mexico

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Forest Ecosystems 2024年第1期11卷 1-13页

作者： Jaime Roberto Padilla-Martínez Carola Paul Kai Husmann Jose Javier Corral-Rivas Klaus von Gadow Department of Forest Economics and Sustainable Land-use Planning Faculty of Forest Sciences and Forest EcologyUniversity of GottingenBüsgenweg 137077GottingenGermany Facultad de Ciencias Forestales Universidad Juarez del Estado de DurangoRío Papaloapan y Boulevard Durango S/N34120DurangoMexico Faculty of Forestry and Forest Ecology Georg-August-UniversitatBüsgenweg 537077GottingenGermany Department of Forestry and Wood Science Faculty of AgriSciencesStellenbosch UniversityStellenbosch Private Bag X1Stellenbosch7602South Africa

Multispecies forests have received increased scientific attention,driven by the hypothesis that biodiversity improves ecological ***,a greater species diversity presents challenges for forest management and *** study aims to develop basal area growth models for tree species *** analysis is based on a dataset of 423 permanent plots(2,500 m^(2))located in temperate forests in Durango,***,we define tree species cohorts based on individual and neighborhood-based variables using a combination of principal component and cluster ***,we estimate the basal area increment of each cohort through the generalized additive model to describe the effect of tree size,competition,stand density and site *** principal component and cluster analyses assign a total of 37 tree species to eight cohorts that differed primarily with regard to the distribution of tree size and vertical position within the *** generalized additive models provide satisfactory estimates of tree growth for the species cohorts,explaining between 19 and 53 percent of the total variation of basal area increment,and highlight the following results:i)most cohorts show a"rise-and-fall"effect of tree size on tree growth;ii)surprisingly,the competition index"basal area of larger trees"had showed a positive effect in four of the eight cohorts;iii)stand density had a negative effect on basal area increment,though the effect was minor in medium-and high-density stands,and iv)basal area growth was positively correlated with site quality except for an oak *** developed species cohorts and growth models provide insight into their particular ecological features and growth patterns that may support the development of sustainable management strategies for temperate multispecies forests.

关键词： Temperate multispecies forests Cluster analysis Basal area increment generalized additive models

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Modelling Mixed-Frequency Time Series with Structural Change

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COMPUTATIONAL ECONOMICS 2025年第6期65卷 3237-3258页

作者： Glova, Adrian Matthew G. Barrios, Erniel B. Monash Univ Malaysia Subang Jaya Selangor Malaysia Univ Philippines Diliman Sch Stat Quezon City Philippines

Predictive ability of time series models is easily compromised in the presence of structural breaks, common among financial and economic variables amidst market shocks and policy regime shifts. We address this problem by estimating a semiparametric mixed-frequency model, that incorporate high frequency data either in the conditional mean or the conditional variance equation. The inclusion of high frequency data through non-parametric smoothing functions complements the low frequency data to capture possible non-linear relationships triggered by the structural change. Simulation studies indicate that in the presence of structural change, the varying frequency in the mean model provides improved in-sample fit and superior out-of-sample predictive ability relative to low frequency time series models. These hold across a broad range of simulation settings, such as varying time series lengths, nature of structural break points, and temporal dependencies. We illustrate the relative advantage of the method in predicting stock returns and foreign exchange rates in the case of the Philippines.

关键词： Mixed frequency data Structural break Semiparametric models generalized additive models

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A utopic adventure in the modelling of conditional univariate and multivariate extremes

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EXTREMES 2025年第1期28卷 173-207页

作者： Belzile, Leo R. Hazra, Arnab Yadav, Rishikesh HEC Montreal Dept Decis Sci 3000chemin Cote Sainte Catherine Montreal PQ H3T 2A7 Canada Indian Inst Technol Kanpur Dept Math & Stat Kanpur 208016 Uttar Pradesh India

This paper presents the contribution of Team Yahabe to the EVA (2023) Conference Data Challenge. We tackle the four problems posed by the organizers by revisiting the current and existing literature on conditional univariate and multivariate extremes. We highlight overarching themes linking the four tasks, ranging from model validation at extremely high quantile levels to building customized estimation strategies that leverage model assumptions.

关键词： Conditional extremes modelling Cross-validation generalized additive models Multivariate generalized Pareto distributions

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