Environmetrics最新文献

{"title":"Bayesian Mediation Analysis With Bayesian Additive Regression Trees","authors":"Qingzhao Yu,&nbsp;Bin Li","doi":"10.1002/env.70125","DOIUrl":"https://doi.org/10.1002/env.70125","url":null,"abstract":"<div>\u0000 \u0000 <p>Understanding how environmental exposures influence health outcomes through biological pathways is critical for environmental risk assessment. We propose a Bayesian mediation analysis framework that integrates Bayesian Additive Regression Trees (BART) to model complex, nonlinear, and high-dimensional relationships among exposures, mediators, and outcomes. The method accommodates multiple mediators, interaction effects, and uncertainty quantification through posterior inference. We introduce two estimation strategies for direct and indirect effects and develop an algorithm for computing the Deviance Information Criterion (DIC) for model evaluation. An open-source R package, <i>bmabart</i>, implements the approach with visualization tools. Simulation studies demonstrate robustness in identifying significant mediators under challenging correlation structures. We apply the method to Louisiana Triple Negative Breast Cancer (TNBC) data, investigating microRNAs that mediate the association between air pollution burden, measured by the Environmental Justice Index, and cancer stage at diagnosis. Results highlight key biomarkers linking environmental exposures to disease progression, offering insights into mechanisms underlying health disparities. This framework provides a flexible and reproducible tool for environmental health research where complex mediation structures are common.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148647818","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}

{"title":"Agreement Coefficients for Continuous Variables: A Review","authors":"Ronny O. Vallejos","doi":"10.1002/env.70117","DOIUrl":"https://doi.org/10.1002/env.70117","url":null,"abstract":"<div>\u0000 \u0000 <p>Agreement coefficients provide a fundamental framework for quantifying the concordance between two or more measurement methods applied to the same continuous variable. Unlike correlation, which measures the strength of a linear relationship, agreement focuses on assessing whether measurements are numerically similar, capturing both precision and accuracy. This review provides a comprehensive overview of the primary statistical approaches for assessing agreement between continuous variables. Such a synthesis is timely, as it has been 15–20 years since the last major review in the field. Beginning with the seminal contributions of Bland and Altman (1986) and Lin (1989), the paper discusses extensions of their methods to robust, multivariate, and repeated-measures settings, as well as recent developments like the probability of agreement and measures based on alternative distance functions measures. Special attention is given to probabilistic and spatial generalizations, including frameworks designed for geostatistical and areal data, which have become increasingly relevant in modern applications such as image analysis and environmental statistics. Through illustrative examples and comparative discussions, this review highlights the evolution, connections, and limitations of existing agreement measures, identifying open challenges and directions for future research.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148647819","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}

{"title":"Quantile Estimation in Rainfall Frequency Analysis With Log-Logistic Distribution Using Pivotal Quantities: Method, Effect, and Application","authors":"Weiqiang Zheng,&nbsp;Shuguang Liu,&nbsp;Zhengzheng Zhou","doi":"10.1002/env.70110","DOIUrl":"https://doi.org/10.1002/env.70110","url":null,"abstract":"<div>\u0000 \u0000 <p>Rainfall frequency analysis commonly presents results as confidence intervals around quantile estimates to account for uncertainty. However, standard bootstrap methods can introduce considerable errors, particularly when sample sizes are small. Focusing on the widely used Log-logistic (LLO) distribution, this study proposes an approach based on pivotal quantities. By generating realizations from three joint pivotal quantities, the method enables the estimation of distribution parameters and return levels along with their associated uncertainties. Its performance is assessed through simulation experiments involving LLO distributions with varying shape parameters, and compared with the basic percentile bootstrap and the bias-corrected and accelerated (BCa) bootstrap methods. The results indicate that the pivotal quantity method generally provides more accurate quantile estimates for both parameters and return levels, while showing lower sensitivity to the characteristics of the underlying distribution. The advantages are particularly evident under conditions commonly encountered in rainfall frequency analysis, such as small sample sizes, high return periods, and extreme confidence levels. The practical utility of the method is illustrated through a case study in Suzhou City, China, which highlights spatial variations in rainfall intensity and underscores the importance of accounting for heavy-tail behavior at rain gauges with large values of the diagnostic variable <i>Q</i>_<i>k</i>. Overall, the proposed pivotal quantity method provides a promising alternative for improving the reliability of statistical inference in rainfall frequency analysis.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616989","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}

{"title":"Short-Term CO\u0000 \u0000 \u0000 \u0000  \u0000 \u0000 2\u0000 \u0000 \u0000 \u0000 $$ {}_2 $$\u0000 Emissions Forecasting: Insight From the Italian Electricity Market","authors":"Pierdomenico Duttilo,&nbsp;Francesco Lisi","doi":"10.1002/env.70126","DOIUrl":"https://doi.org/10.1002/env.70126","url":null,"abstract":"<p>This study investigates the short-term forecasting of carbon emissions from electricity generation in the Italian power market. Using hourly data from 2021 to 2023, several statistical models and forecast combination methods are evaluated and compared at the national and zonal levels. Four main model classes are considered: (i) linear parametric models, such as seasonal autoregressive integrated moving average (SARIMA) and its exogenous variable extension; (ii) functional parametric models, including seasonal functional autoregressive models, with and without exogenous variables; (iii) (semi) nonparametric and possibly nonlinear models, notably the generalized additive model (GAM) and TBATS (trigonometric seasonality, Box-Cox transformation, ARMA errors, trend, and seasonality); and (iv) a semi-functional approach based on the K-nearest neighbors. Forecast combinations include simple averaging, the optimal Bates and Granger weighting scheme, and a selection-based strategy that chooses the best model for each hour. The results show that the GAM produces the most accurate forecasts during the daytime hours, while the functional parametric models perform best in the early morning. Among the combination methods, the simple average and the selection-based approaches consistently outperform all individual models. The findings underscore the value of hybrid forecasting frameworks in improving the accuracy and reliability of short-term carbon emissions predictions in power systems. In addition, they highlight the importance of considering zonal specificities when implementing flexible energy demand strategies. The timing of low-carbon emissions varies between market zones throughout the day.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70126","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616908","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}

{"title":"Compositional Outcomes and Environmental Mixtures: The Dirichlet Bayesian Weighted Quantile Sum Regression","authors":"Hachem Saddiki,&nbsp;Joshua L. Warren,&nbsp;Corina Lesseur,&nbsp;Elena Colicino","doi":"10.1002/env.70120","DOIUrl":"https://doi.org/10.1002/env.70120","url":null,"abstract":"<div>\u0000 \u0000 <p>Environmental mixture approaches do not accommodate compositional outcomes, consisting of vectors constrained onto the unit simplex. This limitation poses challenges in effectively evaluating the associations between multiple concurrent environmental exposures and their respective impacts on this type of outcome. As a result, there is a pressing need for the development of analytical methods that can more accurately assess the complexity of these relationships. Here, we extend the Bayesian weighted quantile sum regression (BWQS) framework for jointly modeling compositional outcomes and environmental mixtures using a Dirichlet distribution with a multinomial logit link function. The proposed approach, named Dirichlet-BWQS (DBWQS), allows for the simultaneous estimation of mixture weights associated with each exposure mixture component as well as the association between the overall exposure mixture index and each outcome proportion. We assess the performance of DBWQS regression on extensive simulated data and a real scenario where we investigate the associations between environmental chemical mixtures and DNA methylation-derived placental cell composition, using publicly available data. Finally, we developed an R package <i>xbwqs</i> where we made our proposed method publicly available.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616948","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}

{"title":"Deep Spatially Varying Coefficient Model for Interpolation of Non-Stationary Meteorological Data","authors":"Tong Wu,&nbsp;Nan Chen,&nbsp;Zhi-Sheng Ye","doi":"10.1002/env.70129","DOIUrl":"https://doi.org/10.1002/env.70129","url":null,"abstract":"<p>In spatial statistics, the spatially varying coefficient model (SVCM) is widely applied in the analysis and interpolation of non-stationary spatial data. By incorporating spatially varying coefficients, the model can capture spatial heterogeneity and provide an attractive interpretation of response-covariate associations. However, intensive matrix operations are inevitable in the inference of SVCM, which limits its scalability to massive spatial datasets. In contrast, deep learning has demonstrated considerable potential in various regression and classification tasks with large-scale data in terms of accuracy and computational efficiency. Nevertheless, many deep learning models fail to capture the spatial variability when directly applied to non-stationary spatial data and often lack interpretability. To address these challenges, we propose a deep spatially varying coefficient model (DSVCM) that combines the strengths of SVCM and deep learning. The proposed model aims to provide a computationally efficient interpolation method for non-stationary spatial data, while also facilitating the interpretation of the effects of input covariates. In the proposed model, a deep neural network (DNN) is utilized to estimate spatially varying coefficients and responses, where spatial basis functions are served as input to capture spatial variability. By leveraging spatial basis functions, we establish the connection between DSVCM and SVCM, and theoretically prove the superiority of DSVCM in prediction accuracy. The effectiveness of the model is further demonstrated through extensive simulation studies and experiments on Singapore air temperature data. The results show that the proposed DSVCM outperforms several baseline models for spatial interpolation in both prediction accuracy and computational efficiency.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70129","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616934","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}

{"title":"R-Learner Data Integration for Estimating Population Trends From Opportunistic and Structured Survey Data","authors":"Andrew B. Whetten,&nbsp;Renata M. Diaz,&nbsp;Wesley M. Hochachka,&nbsp;Andrew Stillman,&nbsp;Courtney Lynn Davis,&nbsp;Tom Auer,&nbsp;Matt Strimas-Mackey,&nbsp;Viviana Ruiz-Gutierrez,&nbsp;Paige E. Howell,&nbsp;Eric Kershner,&nbsp;Daniel Fink","doi":"10.1002/env.70116","DOIUrl":"https://doi.org/10.1002/env.70116","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurately quantifying wildlife population change is essential for assessing ecosystem health and guiding conservation action. Model-based integration of structured and opportunistic survey data can expand the spatial scope and improve the precision of trend estimates but requires methods that reconcile differences in observation processes, sampling coverage, and data volume across surveys. We present the integrated R-learner trend model, a modular framework for estimating spatially explicit trends from multiple surveys. In Stage 1, the double machine learning (DML) trend model is applied separately to each survey, isolating ecological signals from intra- and interannual confounding. In Stage 2, survey-specific trends are combined using the R-learner framework, with weights based on sampling coverage probabilities estimated via a spatially explicit mixture-distribution sub-model. This two-stage design enables bias mitigation, reconciles differences in observation processes and coverage, handles high-dimensional feature sets, and balances influence across datasets of differing size and quality. Using simulations, we show that incorporating mixture-distribution weighting improves accuracy and statistical power relative to independent survey-specific models. We then apply our model to data from the North American Breeding Bird Survey and the eBird participatory science project, demonstrating how complementary coverage from structured and opportunistic surveys can be leveraged to produce high-resolution, spatially explicit bird population trends.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616412","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}

{"title":"A Functional Principal Component Analysis Framework for Vibration-Based Structural Condition Assessment of Highway Bridge Spans","authors":"Gianna Agrò,&nbsp;Mariantonietta Ruggieri","doi":"10.1002/env.70124","DOIUrl":"https://doi.org/10.1002/env.70124","url":null,"abstract":"<p>The management of lifespan and safety performance, aimed at ensuring the structural integrity and durability of highway infrastructure, has become an increasingly important area of research. In civil engineering, traditional methods for evaluating dynamic behavior, such as Operational Modal Analysis and the Peak Picking technique, often fall short in detecting subtle signs of damage. This study proposes a novel approach based on Functional Principal Component Analysis (FPCA) to enhance the sensitivity of vibration-based structural condition and anomaly assessment. A preliminary analysis based on classical PCA applied to the same dataset was previously presented in Agrò et al. (2025a). That study highlighted the capability of PCA to capture dominant variance patterns in the vibration signals. However, PCA operates on discretized time series and does not explicitly account for their functional nature. In contrast, the FPCA framework proposed in this study models the signals as continuous functions, enabling a more refined characterization of temporal variability and improving sensitivity to subtle structural differences. By comparing the optimal functional subspaces derived from FPCA for both intact and potentially compromised bridge spans, we quantify structural differences through the cosine of principal angles. The approach is applied to a real case involving a viaduct on the Palermo–Catania highway, demonstrating its potential to reveal anomalies not evident through conventional modal analysis.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70124","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616415","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}

{"title":"Hierarchical Spatio-Temporal Model Under \u0000 \u0000 \u0000 t\u0000 \u0000 $$ t $$\u0000 -Process With Application","authors":"Chunzheng Cao,&nbsp;Wenzhu Chen,&nbsp;Xiaoxin Zhu","doi":"10.1002/env.70123","DOIUrl":"https://doi.org/10.1002/env.70123","url":null,"abstract":"<div>\u0000 \u0000 <p>Gaussian process has been widely used in spatio-temporal models to analyze and reveal the underlying correlation of spatio-temporal data. Nevertheless, inferences drawn from Gaussian process-based models can be highly sensitive to outliers or distributional misspecifications. To enhance robustness, we propose a robust hierarchical spatio-temporal model based on Student-<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>t</mi>\u0000 </mrow>\u0000 <annotation>$$ t $$</annotation>\u0000 </semantics></math> process framework to enable reliable inferences under data contamination. The proposed model employs two independent <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>t</mi>\u0000 </mrow>\u0000 <annotation>$$ t $$</annotation>\u0000 </semantics></math>-processes to respectively describe the latent spatial random effects and random errors, thereby ensuring robust inferences against various types of outliers. A variational Expectation-Maximization algorithm is developed for efficient parameter estimation and prediction. Simulation studies demonstrate that the proposed model delivers reliable and accurate results even when the data are contaminated by substantial outliers. The practical usefulness of the model is further illustrated through an application to <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> concentration data, highlighting its effectiveness in robust spatio-temporal inference.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615855","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}

{"title":"Multi-Step Forecasting of Wind Speed and Solar Irradiance Using Lag-Optimized Stochastic Stacked LSTM Networks","authors":"Brahim Taoussi,&nbsp;Chahtou Amina,&nbsp;Fares Sofiane Mazouni","doi":"10.1002/env.70122","DOIUrl":"https://doi.org/10.1002/env.70122","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;p&gt;The rapid expansion of solar PV and wind generation intensifies the challenge of day-ahead grid planning under stochastic meteorological conditions. While the literature is dominated by sub-day forecasting studies, day-ahead predictions remain critical for unit commitment and grid scheduling. This study proposes a stochastic stacked Long Short-Term Memory (LSTM) framework, tuned via Bayesian optimization, for simultaneous day-ahead forecasting (96 step-ahead) of wind speed and solar irradiation using high-resolution 15-min measurements from the Adrar 20 MW solar PV facility in Algeria. Four input strategies were rigorously evaluated: full autoregressive lags (S-LSTM), Random Forest importance-based lag selection (RF-LSTM), engineered auxiliary features (AUX-RF-LSTM), and exogenous meteorological variables (EXG-RF-LSTM), benchmarked against Holt-Winters and naïve persistence baselines. The parsimonious RF-LSTM architecture demonstrated superior performance across all evaluation frameworks. In deterministic point forecasting, it achieved the highest accuracy for both variables (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msup&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;R&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mn&gt;85&lt;/mn&gt;\u0000 &lt;mo&gt;.&lt;/mo&gt;\u0000 &lt;mn&gt;12&lt;/mn&gt;\u0000 &lt;mo&gt;%&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {R}^2=85.12% $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; for wind speed and &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;97&lt;/mn&gt;\u0000 &lt;mo&gt;.&lt;/mo&gt;\u0000 &lt;mn&gt;15&lt;/mn&gt;\u0000 &lt;mo&gt;%&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ 97.15% $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; for solar irradiation) while substantially reducing residual serial dependence relative to the Holt-Winters benchmark. These gains were confirmed as statistically significant via the Harvey-Leybourne-Newbold corrected Diebold-Mariano test across all pairwise comparisons. In probabilistic forecasting via Monte Carlo Dropout, the RF-LSTM achieved near-perfect calibration for solar irradiation (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;P&lt;/mi&gt;\u0000 &lt;mi&gt;I&lt;/mi&gt;\u0000 &lt;mi&gt;C&lt;/mi&gt;\u0000 &lt;mi&gt;P&lt;/mi&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mn&gt;95&lt;/mn&gt;\u0000 &lt;mo&gt;.&lt;/mo&gt;\u0000 &lt;mn&gt;10&lt;/mn&gt;\u0000 &lt;mo&gt;%&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ PICP=95.10% $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;) and acceptable risk-aware uncertainty boundaries for wind profiles. Interestingly, expanding the input space with engineered or exogenous features systematically degrad","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148534039","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}