Environmetrics最新文献

Linear Assignment Sampling: Spatially Balanced Sampling With Auxiliary Variables 线性分配抽样：具有辅助变量的空间平衡抽样

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2025-10-01 DOI: 10.1002/env.70042

B. L. Robertson, C. J. Price, M. Reale

引用次数: 0

The Impact of Climatic Factors on Respiratory Pharmaceutical Demand: A Comparison of Forecasting Models for Greece 气候因素对呼吸药品需求的影响：希腊预测模型的比较

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2025-09-22 DOI: 10.1002/env.70041

Viviana Schisa, Matteo Farnè

{"title":"The Impact of Climatic Factors on Respiratory Pharmaceutical Demand: A Comparison of Forecasting Models for Greece","authors":"Viviana Schisa, Matteo Farnè","doi":"10.1002/env.70041","DOIUrl":"https://doi.org/10.1002/env.70041","url":null,"abstract":"<p>Climate change is increasingly recognized as a driver of health-related outcomes, yet its impact on pharmaceutical demand remains largely understudied. As environmental conditions evolve and extreme weather events intensify, anticipating their influence on medical needs is essential for designing resilient healthcare systems. This study examines the relationship between climate variability and the weekly demand for respiratory prescription pharmaceuticals in Greece, based on a dataset spanning seven and a half years (390 weeks). Granger-causality spectra are employed to explore potential causal relationships. Following variable selection, four forecasting models are implemented: Prophet, a Vector Autoregressive model with exogenous variables (VARX), Random Forest with Moving Block Bootstrap (MBB-RF), and Long Short-Term Memory (LSTM) networks. The MBB-RF model achieves the best performance in relative error metrics while providing robust insights through variable importance rankings. The LSTM model outperforms most metrics, highlighting its ability to capture nonlinear dependencies. The VARX model, which includes Prophet-based exogenous inputs, balances interpretability and accuracy, although it is slightly less competitive in overall predictive performance. These findings underscore the added value of climate-sensitive variables in modeling pharmaceutical demand and provide a data-driven foundation for adaptive strategies in healthcare planning under changing environmental conditions.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 7","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70041","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145111089","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

Skew Gaussian Markov Random Fields Under Decomposable Graphs 可分解图下的偏高斯马尔可夫随机场

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2025-09-10 DOI: 10.1002/env.70039

Hamid Zareifard, Majid Jafari Khaledi

引用次数: 0

Using Expected Improvement of Gradients for Robotic Exploration of Ocean Salinity Fronts 基于期望改进梯度的海洋盐度锋机器人探测

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2025-09-07 DOI: 10.1002/env.70037

André Julius Hovd Olaisen, Yaolin Ge, Jo Eidsvik

{"title":"Using Expected Improvement of Gradients for Robotic Exploration of Ocean Salinity Fronts","authors":"André Julius Hovd Olaisen, Yaolin Ge, Jo Eidsvik","doi":"10.1002/env.70037","DOIUrl":"https://doi.org/10.1002/env.70037","url":null,"abstract":"<div>\u0000 \u0000 <p>We develop, test, and deploy a sampling design strategy that enables an autonomous underwater vehicle (AUV) to explore and detect large gradients in spatio-temporal random fields. Our approach models the field using a Gaussian random field, which means that the directional derivatives of the field are Gaussian distributed. Leveraging fast matrix factorization and data thinning techniques, we obtain real-time data assimilation and design evaluation onboard the AUV. At each stage in the dynamic framework, possible design transects are formed based on a spider-leg search space pattern, and the agent chooses the optimal design for the next stage. The design criterion used is based on expected improvement (EI) in directional derivatives. This means that we compute the expected value of observing a larger derivative than what has been seen already. EI is among the most popular acquisition functions in Bayesian optimization. To evaluate the effectiveness of this approach, we conduct a simulation study comparing EI with alternative selection criteria. Our algorithm was embedded on an AUV which was deployed for characterizing a river plume frontal system in a Norwegian fjord. Using EI in the salinity field derivatives, the vehicle successfully sampled the fjord for approximately 2 h without human intervention in two separate field experiments.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145012156","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

Correction to “Estimation of Impact Ranges for Functional Valued Predictors” 修正“估计函数值预测因子的影响范围”

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2025-09-03 DOI: 10.1002/env.70040

引用次数: 0

Spatial Modeling of Extremes and an Angular Component 极值空间建模和角分量

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2025-09-02 DOI: 10.1002/env.70025

G. Tamagny, M. Ribatet

引用次数: 0

Causal Discovery in Multivariate Extremes: A Study of Swiss Hydrological Catchments 多元极端的因果发现：瑞士水文集水区的研究

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2025-08-25 DOI: 10.1002/env.70034

L. Mhalla, V. Chavez-Demoulin, P. Naveau

引用次数: 0

Estimating Extreme Wave Surges in the Presence of Missing Data 在缺少数据的情况下估计极端浪涌

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2025-08-17 DOI: 10.1002/env.70036

James H. McVittie, Orla A. Murphy

引用次数: 0

Combined Quantile Forecasting for High-Dimensional Non-Gaussian Data 高维非高斯数据的组合分位数预测

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2025-08-14 DOI: 10.1002/env.70035

Seeun Park, Hee-Seok Oh, Yaeji Lim

{"title":"Combined Quantile Forecasting for High-Dimensional Non-Gaussian Data","authors":"Seeun Park, Hee-Seok Oh, Yaeji Lim","doi":"10.1002/env.70035","DOIUrl":"https://doi.org/10.1002/env.70035","url":null,"abstract":"<div>\u0000 \u0000 <p>This study proposes a novel method for forecasting a scalar variable based on high-dimensional predictors that is applicable to various data distributions. In the literature, one of the popular approaches for forecasting with many predictors is to use factor models. However, these traditional methods are ineffective when the data exhibit non-Gaussian characteristics such as skewness or heavy tails. In this study, we newly utilize a quantile factor model to extract quantile factors that describe specific quantiles of the data beyond the mean factor. We then build a quantile-based forecast model using the estimated quantile factors at different quantile levels as predictors. Finally, the predicted values at various quantile levels are combined into a single forecast as a weighted average with weights determined by a Markov chain based on past trends of the target variable. The main idea of the proposed method is to effectively incorporate a quantile approach into a forecasting method to handle non-Gaussian characteristics. The performance of the proposed method is evaluated through a simulation study and real data analysis of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mtext>PM</mtext>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>2</mn>\u0000 <mo>.</mo>\u0000 <mn>5</mn>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {mathrm{PM}}_{2.5} $$</annotation>\u0000 </semantics></math> data in South Korea, where the proposed method outperforms other existing methods in most cases.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144843386","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 Multivariate Space-Time Dynamic Model for Characterizing the Atmospheric Impacts Following the Mt. Pinatubo Eruption 表征皮纳图博火山喷发后大气影响的多元时空动态模型

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2025-08-12 DOI: 10.1002/env.70030

Robert C. Garrett, Lyndsay Shand, Gabriel Huerta

{"title":"A Multivariate Space-Time Dynamic Model for Characterizing the Atmospheric Impacts Following the Mt. Pinatubo Eruption","authors":"Robert C. Garrett, Lyndsay Shand, Gabriel Huerta","doi":"10.1002/env.70030","DOIUrl":"https://doi.org/10.1002/env.70030","url":null,"abstract":"<p>The June 1991 Mt. Pinatubo eruption resulted in a massive increase of sulfate aerosols in the atmosphere, absorbing radiation and leading to global changes in surface and stratospheric temperatures. A volcanic eruption of this magnitude serves as a natural analog for stratospheric aerosol injection, a proposed solar radiation modification method to combat a warming climate. The impacts of such an event are multifaceted and region-specific. Our goal is to characterize the multivariate and dynamic nature of the atmospheric impacts following the Mt. Pinatubo eruption. We developed a multivariate space-time dynamic linear model to understand the full extent of the spatially- and temporally-varying impacts. Specifically, spatial variation is modeled using a flexible set of basis functions for which the basis coefficients are allowed to vary in time through a vector autoregressive (VAR) structure. This novel model is cast in a Dynamic Linear Model (DLM) framework and estimated via a customized MCMC approach. We demonstrate how the model quantifies the relationships between key atmospheric parameters prior to and following the Mt. Pinatubo eruption with reanalysis data from MERRA-2 and highlight when such a model is advantageous over univariate models.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144814643","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