Environmetrics最新文献_第2页

Time-Aware UNet and Super-Resolution Deep Residual Networks for Spatial Downscaling 空间降尺度的时间感知UNet和超分辨率深度残差网络

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2026-08-26 DOI: 10.1002/env.70134

Mika Sipilä, Sabrina Maggio, Sandra De Iaco, Klaus Nordhausen, Monica Palma, Sara Taskinen

引用次数: 0

Enhancing Identifiable Variational Autoencoder in the Presence of Missing Values and Auxiliary Covariates for Spatio-Temporal Ozone Modeling 在缺失值和辅助协变量存在下增强可识别变分自编码器用于臭氧时空模拟

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2026-08-24 DOI: 10.1002/env.70121

Mika Sipilä, Sandra De Iaco, Claudia Cappello, Klaus Nordhausen, Monica Palma, Sara Taskinen

{"title":"Enhancing Identifiable Variational Autoencoder in the Presence of Missing Values and Auxiliary Covariates for Spatio-Temporal Ozone Modeling","authors":"Mika Sipilä, Sandra De Iaco, Claudia Cappello, Klaus Nordhausen, Monica Palma, Sara Taskinen","doi":"10.1002/env.70121","DOIUrl":"https://doi.org/10.1002/env.70121","url":null,"abstract":"<p>The formation of ground-level ozone follows complex nonlinear photochemical processes that depend on multiple environmental factors and have strong spatio-temporal structures. Environmental data used to study these dynamics usually originate from multiple sources, including in situ monitoring stations and satellite observations. While in situ data provide more accurate measurements, they often suffer from missing values and limited coverage, making it beneficial to incorporate additional satellite-based covariates. To address these challenges for spatio-temporal interpolation and forecasting aims, novel identifiable variational autoencoder (iVAE)-based methods are introduced that explicitly integrate satellite-derived predictors and handle missing values within a nonlinear blind source separation framework. The proposed extensions preserve the identifiability guarantees of the iVAE framework under the missing at random assumption. Performance is benchmarked against established statistical and deep learning approaches by using daily average ozone concentrations in Northern Italy: in interpolation, the proposed method achieves an appreciable reduction of the estimation errors and in forecasting it shows 10 times less training time with respect to the best competing method. The results establish nonlinear blind source separation as a promising approach for spatio-temporal prediction of environmental data.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70121","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Demystifying Spatial Confounding—A Unified Analytical Framework for the Bias in Covariate Effect Estimates 揭开空间混淆的神秘面纱——协变量效应估计偏差的统一分析框架

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2026-08-24 DOI: 10.1002/env.70128

Emiko Dupont, Isa Marques, Thomas Kneib

{"title":"Demystifying Spatial Confounding—A Unified Analytical Framework for the Bias in Covariate Effect Estimates","authors":"Emiko Dupont, Isa Marques, Thomas Kneib","doi":"10.1002/env.70128","DOIUrl":"https://doi.org/10.1002/env.70128","url":null,"abstract":"<p>In spatial regression models, unmeasured spatial variables, represented by spatial random effects, are typically not independent of observed covariates and can induce significant bias in covariate effect estimates. This fundamental problem, known as spatial confounding, has been extensively studied, but sometimes with puzzling and seemingly contradictory results. Here, we introduce a broad theoretical framework that brings mathematical clarity to spatial confounding. Our focus is the finite-sample bias affecting simulation results and practical applications. Our intention is not to challenge existing results in the literature, but rather to build intuition and provide a unifying perspective that helps explain and connect them. We derive a compact analytical expression where, in the metric induced by the precision structure of the chosen analysis model, the bias is expressed in terms of the empirical correlation between a covariate and its confounder and their relative sizes. Thus, our formulation directly identifies these relationships as the key driver of bias; moreover, using the eigendecomposition of the precision structure, we obtain detailed quantitative characterizations of its behavior. The eigendecomposition has a natural interpretation as spatial frequencies and nonspatial information, with the eigenvalues encoding the precise effect of spatial smoothing in the model. This allows the insights from our bias expression to be translated into interpretable scenarios and explain subtle and counter-intuitive behaviors. Finally, we propose a general strategy for addressing the bias in practice. When a covariate has nonspatial information, we show that a general form of the so-called spatial+ method can reduce the bias. If not, explicit assumptions for identifiability are needed, and we develop a procedure in which multiple capped versions of spatial+ are applied. We illustrate our approach with an application to air temperature in Germany.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70128","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analysis of Count Time Series Through Integer-Valued Autoregressive (INAR) Process With Zero-Inflation and Seasonality 具有零膨胀和季节性的整值自回归过程分析计数时间序列

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2026-08-21 DOI: 10.1002/env.70140

Subhankar Chattopadhyay, Atanu Biswas, Samarjit Das, Raju Maiti, Jing-Shiang Hwang

{"title":"Analysis of Count Time Series Through Integer-Valued Autoregressive (INAR) Process With Zero-Inflation and Seasonality","authors":"Subhankar Chattopadhyay, Atanu Biswas, Samarjit Das, Raju Maiti, Jing-Shiang Hwang","doi":"10.1002/env.70140","DOIUrl":"https://doi.org/10.1002/env.70140","url":null,"abstract":"<div>\u0000 \u0000 <p>In this article, we study a count time-series data of weekly dengue cases in Kaohsiung City, Taiwan during the period 2009–2012, where the problems of a large number of zeros or zero-inflation and seasonality arise together. To capture both features, we consider an application-oriented zero-inflated seasonal PINAR framework based on zero-inflated Poisson innovations. We also incorporate an exogenous variable, namely the weekly maximum temperature, in the innovations to study the effect of temperature on the number of dengue cases as many studies have found that temperature is a significant factor in spreading dengue infections globally. The proposed model can capture both the non-recovery cases from the previous time point and the new cases coming at the current time point. The distributional and forecasting properties of the proposed model are derived. The consistency and asymptotic normality of the CLS estimator are established under suitable regularity conditions, and simulation experiments are used to examine the finite-sample estimation and forecasting performance of the proposed model. The proposed model is compared with some existing INAR models. Finally, we analyze the data of weekly dengue cases in Kaohsiung for practical illustration.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Directional Asymmetry in Edge-Based Spatial Models via a Skew–Normal Prior 基于斜正态先验的边缘空间模型的方向不对称性

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2026-08-16 DOI: 10.1002/env.70133

Danna L. Cruz-Reyes, Renato M. Assunção, Reinaldo B. Arellano-Valle, Rosangela H. Loschi

{"title":"Directional Asymmetry in Edge-Based Spatial Models via a Skew–Normal Prior","authors":"Danna L. Cruz-Reyes, Renato M. Assunção, Reinaldo B. Arellano-Valle, Rosangela H. Loschi","doi":"10.1002/env.70133","DOIUrl":"https://doi.org/10.1002/env.70133","url":null,"abstract":"<div>\u0000 \u0000 <p>We introduce a skewed edge-based spatial prior, named RENeGe-sk, that extends the Gaussian RENeGe framework by incorporating directional asymmetry through a skew–normal distribution. Skewness is defined on the edge graph and propagated to the node space, aligning asymmetric behavior with transitions across neighboring regions rather than with marginal node effects. The model is formulated within the skew–normal framework and employs identifiable hierarchical priors together with low-rank parameterizations to ensure scalability. The skew–normal's stochastic representation is considered to facilitate the computational implementation. Simulation studies show that RENeGe-sk recovers compact, edge-align directional structure more accurately than symmetric Gaussian priors, while remaining competitive under irregular spatial patterns. An application to cancer incidence data in Southern Brazil illustrates how the proposed approach yields stable area-level estimates while preserving localized, directionally driven spatial variation.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148783584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian Inference for the Automultinomial Model With an Application to Landcover Data 土地覆盖数据自动多项模型的贝叶斯推断

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2026-08-13 DOI: 10.1002/env.70131

Maria Paula Duenas-Herrera, Stephen Berg, Murali Haran

{"title":"Bayesian Inference for the Automultinomial Model With an Application to Landcover Data","authors":"Maria Paula Duenas-Herrera, Stephen Berg, Murali Haran","doi":"10.1002/env.70131","DOIUrl":"https://doi.org/10.1002/env.70131","url":null,"abstract":"<p>Multicategory lattice data arise in a wide variety of disciplines such as image analysis, biology, and forestry. We consider modeling such data with the automultinomial model, which can be viewed as a natural extension of the autologistic model to multicategory responses, or equivalently as an extension of the Potts model that incorporates covariate information into a pure-intercept model. The automultinomial model has the advantage of having a unique parameter that controls the spatial correlation. However, the model's likelihood involves an intractable normalizing function of the model parameters that poses serious computational problems for likelihood-based inference. We address this difficulty by performing Bayesian inference through the Double–Metropolis Hastings algorithm, and implement diagnostics to assess the convergence to the target posterior distribution. Through simulation studies and an application to land cover data, we find that the automultinomial model is flexible across a wide range of spatial correlations while maintaining a relatively simple specification. For large data sets, we find it also has advantages over spatial generalized linear mixed models. To make this model practical for scientists, we provide recommendations for its specification and computational implementation.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70131","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148753727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling GHG Emissions Across Selected South Asian Countries Using a Unit Log-Log Quantile Mixed Model 使用单位对数-对数分位数混合模型模拟选定南亚国家的温室气体排放

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2026-08-11 DOI: 10.1002/env.70132

Nirajan Bam

{"title":"Modeling GHG Emissions Across Selected South Asian Countries Using a Unit Log-Log Quantile Mixed Model","authors":"Nirajan Bam","doi":"10.1002/env.70132","DOIUrl":"https://doi.org/10.1002/env.70132","url":null,"abstract":"<p>This study examines greenhouse gas (GHG) emissions in South Asian countries by analyzing both their proportion relative to global emissions and total emissions, along with key country-level determinants. To address the bounded (0-1) and correlated nature of the GHG proportion, this study proposes a new Bayesian Unit Log-Log Quantile Mixed Model (ULLQMM), and its performance was evaluated through Monte Carlo simulation. The ULLQMM is applied to investigate the associations between GHG proportion and time, population size, log(GDP), urban population growth, and renewable energy share in selected South Asian countries. Results show that time, population size, and urban population growth are positively associated with the proportion of GHG emissions, whereas renewable energy share and log(GDP) are negatively associated with the proportion of GHG emissions. Additionally, the association between total GHG emissions and key determinants: time, population size, log(GDP), urban population growth, and renewable energy share was analyzed using a Bayesian general linear mixed model. The results show that time, population size, and log(GDP) are positively associated with total GHG emissions, whereas renewable energy share and urban population growth are negatively associated. These findings underscore the critical role of demographic, economic, and energy-related factors in shaping GHG emissions and demonstrate the potential of renewable energy expansion to reduce GHG emissions in South Asia.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70132","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148753312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatial-Awared Deep Echo State Networks for High-Resolution Spatio-Temporal Wind Speed Nowcasting 高分辨率时空风速临近预报的空间感知深度回波状态网络

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2026-08-07 DOI: 10.1002/env.70119

Zipei Geng, Stefano Castruccio, Marc G. Genton

{"title":"Spatial-Awared Deep Echo State Networks for High-Resolution Spatio-Temporal Wind Speed Nowcasting","authors":"Zipei Geng, Stefano Castruccio, Marc G. Genton","doi":"10.1002/env.70119","DOIUrl":"https://doi.org/10.1002/env.70119","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurate wind speed nowcasting is crucial for optimizing wind energy production and grid stability, especially for countries such as Saudi Arabia with ambitious renewable energy targets. This article introduces a novel deep learning framework combining Deep Echo State Networks (DESNs) with spatial information through <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>k</mi>\u0000 </mrow>\u0000 <annotation>$$ k $$</annotation>\u0000 </semantics></math>-nearest neighbors for high-resolution wind speed prediction. Specifically, we develop a Spatio-Temporal Attention Graph Autoencoder (STAGA) that effectively reduces spatial dimensionality while preserving critical spatio-temporal patterns, enabling efficient processing of large-scale meteorological data. Experiments using high-resolution simulated wind data from Saudi Arabia demonstrate that our model consistently outperforms traditional methods. In addition, we provide uncertainty quantification through conformal prediction and demonstrate its practical value by assessing wind power estimates. The proposed methodology offers significant improvements for operational wind forecasting systems, supporting the efficient integration of wind energy into power grids.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multiple Change Point Detection for Nonstationary Network Autoregressive Models With Node-Level Dynamics 节点级动态非平稳网络自回归模型的多变化点检测

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2026-08-04 DOI: 10.1002/env.70130

Ruishan Lin, Abolfazl Safikhani

{"title":"Multiple Change Point Detection for Nonstationary Network Autoregressive Models With Node-Level Dynamics","authors":"Ruishan Lin, Abolfazl Safikhani","doi":"10.1002/env.70130","DOIUrl":"https://doi.org/10.1002/env.70130","url":null,"abstract":"<div>\u0000 \u0000 <p>Understanding temporal dynamics in complex systems often requires identifying abrupt structural changes, known as change points in multivariate time series. Traditional vector autoregressive (VAR) models have been widely used for modeling dependencies across time, yet their parameter space grows quadratically with the number of variables, leading to computational and estimation challenges in high-dimensional settings. The recently proposed network autoregressive (NAR) modeling framework offers a computationally efficient alternative by reducing parameter complexity through a network-based representation. However, existing NAR models either assume homogeneous temporal behavior across all nodes, overlooking node-specific dynamics that frequently arise in environmental and socio-economic systems, or do not allow for structural breaks. In this work, we propose <i>NLDNAR-CP</i>, a novel change point detection method within a node-specific NAR framework that accommodates heterogeneous temporal dependencies across variables. The proposed approach efficiently detects multiple structural breaks while preserving scalability to high-dimensional networks. We demonstrate the method's superior empirical performance through extensive simulations and a real-world environmental application.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Spatial Mean-Mixture Gaussian Model for Skewed Geostatistical Data 倾斜地统计数据的空间平均-混合高斯模型

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2026-08-02 DOI: 10.1002/env.70127

Bahman Hamidian, Hossein Baghishani, Negar Eghbal, Jamil Ownuk

{"title":"A Spatial Mean-Mixture Gaussian Model for Skewed Geostatistical Data","authors":"Bahman Hamidian, Hossein Baghishani, Negar Eghbal, Jamil Ownuk","doi":"10.1002/env.70127","DOIUrl":"https://doi.org/10.1002/env.70127","url":null,"abstract":"<div>\u0000 \u0000 <p>Gaussian random fields (GRFs) are often used to model spatial dependence in many geostatistical applications. However, environmental data, such as water quality measurements, often exhibit substantial skewness which violates the Gaussian assumption. Ignoring this skewness can result in biased parameter estimates and unreliable spatial predictions. To address this limitation, we propose a spatial extension of the multivariate mean-mixture Gaussian (MMG) distribution that accommodates unbounded skewness while preserving a coherent spatial dependence structure. Our approach incorporates a global latent mixing variable to induce marginal skewness and utilizes a Gaussian process to capture spatial correlation. Identifiability is ensured by a centering transformation and replicated spatial observations, which separate mixture variance from spatial covariance. Model estimation is performed using a generalized expectation-maximization (GEM) and quasi-Newton algorithm, providing stable, closed-form updates for both regression and skewness parameters. We evaluate the model through simulation studies and apply it to highly skewed geo-referenced electrical conductivity (EC) data from groundwater monitoring stations in Golestan Province, Iran, with the primary aim of predicting values at unknown spatial locations. The results demonstrate substantial improvements over standard Gaussian and transformed Gaussian models in both estimation and prediction accuracy.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0