Modeling Spatial Data with Heteroscedasticity Using PLVCSAR Model: A Bayesian Quantile Regression Approach by Rongshang Chen, Zhiyong Chen

“…We apply a Bayesian quantile regression (BQR) of the partially linear varying coefficient spatial autoregressive (PLVCSAR) model for spatial data to improve the prediction of performance. …”
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Remotely Sensed Variables Predict Grassland Diversity Better at Scales Below 1,000 km as Opposed to Abiotic Variables That Predict It Better at Larger Scales by Yujin Zhao, Bernhard Schmid, Zhaoju Zheng, Yang Wang, Jin Wu, Yao Wang, Ziyan Chen, Xia Zhao, Dan Zhao, Yuan Zeng, Yongfei Bai

“…Here we used vegetation survey data from 1,609 field sites (>4,000 plots of 1 m2), remotely sensed data (ecosystem productivity and phenology, habitat heterogeneity, functional traits and spectral diversity), and abiotic data (water‐ and energy‐related, characterizing climate‐dominated environment) together with machine learning and spatial autoregressive models to predict and map grassland species richness per 100 m2 across the Mongolian Plateau at 500 m resolution. …”
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GIS-based calculation method to predict mining subsidence in flat and inclined mining: A comparative case study by Ibrahim Djamaluddin, Poppy Indrayani, Yue Cai, Yujing Jiang

“…Many calculation models are used to predict mining subsidence. A comprehensive method to render current calculation models superfluous can only come from a theoretical model, but the challenge remains in defining the parameters, given the great variety of rock structures found. …”
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Fire Intensity and spRead forecAst (FIRA): A Machine Learning Based Fire Spread Prediction Model for Air Quality Forecasting Application by Wei‐Ting Hung, Barry Baker, Patrick C. Campbell, Youhua Tang, Ravan Ahmadov, Johana Romero‐Alvarez, Haiqin Li, Jordan Schnell

“…FIRA aims to improve the performance of AQF models by providing realistic, dynamic fire characteristics including the spatial distribution and intensity of fire radiative power (FRP). …”
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Evaluation and Prediction of Wind Power Utilization Efficiency Based on Super-SBM and LSTM Models: A Case Study of 30 Provinces in China by Chengyu Li, Qunwei Wang, Peng Zhou

“…This study establishes the improved super-efficiency slack-based measure (Super-SBM) model and long short-term memory (LSTM) network models, systematically and comprehensively measures and predicts the wind power utilization efficiency of 30 regions in China from 2013 to 2020, and explores regional differences in wind power utilization efficiency. …”
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Modeling spatial distributions of Amah Mutsun priority cultural plants to support Indigenous cultural revitalization by Annalise Taylor, Alexii Sigona, Maggi Kelly

“…We utilized community science datasets with an ensemble modeling approach that combines the results of five machine learning models to predict not only the distribution of each species, but also the relative certainty of those predictions spatially. …”
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Spatiotemporal Multivariate Weather Prediction Network Based on CNN-Transformer by Ruowu Wu, Yandan Liang, Lianlei Lin, Zongwei Zhang

“…However, the existing data-based weather prediction methods cannot adequately capture the spatial and temporal evolution characteristics of the target region, which makes it difficult for the existing methods to meet practical application requirements in terms of efficiency and accuracy. …”
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Slope Deformation Prediction Combining Particle Swarm Optimization-Based Fractional-Order Grey Model and <i>K</i>-Means Clustering by Zhenzhu Meng, Yating Hu, Shunqiang Jiang, Sen Zheng, Jinxin Zhang, Zhenxia Yuan, Shaofeng Yao

“…Additionally, we employ a <i>k</i>-means clustering technique to account for both temporal and spatial variations in multi-point monitoring data, which improves the model’s ability to capture the relationships between monitoring points and increases prediction relevance. …”
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High-Resolution Daily XCH₄ Prediction Using New Convolutional Neural Network Autoencoder Model and Remote Sensing Data by Mohamad M. Awad, Saeid Homayouni

“…To mitigate these limitations, a novel Convolutional Neural Network Autoencoder (CNN-AE) model was developed. Validation was performed using the Total Carbon Column Observing Network (TCCON), providing a benchmark for evaluating the accuracy of various interpolation and prediction models. …”
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Interpretation of Bayesian-optimized deep learning models for enhancing soil erosion susceptibility prediction and management: a case study of Eastern India by Meshel Alkahtani, Javed Mallick, Saeed Alqadhi, Md Nawaj Sarif, Mohamed Fatahalla Mohamed Ahmed, Hazem Ghassan Abdo

“…Addressing this issue requires advanced predictive models that can accurately identify areas at risk and inform soil conservation strategies. …”
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A Novel Ionospheric Inversion Model: PINN‐SAMI3 (Physics Informed Neural Network Based on SAMI3) by Jiayu Ma, Haiyang Fu, J. D. Huba, Yaqiu Jin

“…The model incorporates the governing equations of the ionospheric physical model SAMI3 into the neural network to reconstruct the temporal‐spatial distribution of ionospheric plasma parameters. …”
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Prediction of Complex Observed Shear Wave Splitting Patterns at Ryukyu Subduction Zone Using a Strong Intra‐Slab Anisotropy Model by Sharmila Appini, Jiaxuan Li, Hao Hu, Neala Creasy, Leon Thomsen, Joseph McNease, Yingcai Zheng

“…For the same earthquake, the measured splitting patterns also vary spatially across the southwest Japan. Using full‐wave seismic modeling, we showed that a dipping slab with ∼30% shear anisotropy of the tilted transverse isotropy (TTI) type, with a symmetry axis perpendicular to the slab interface, can predict the observed delay times and polarization rotation. …”
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Improving Distribution Prediction by Integrating Expert Range Maps and Opportunistic Occurrences: Evidence From Japanese Sea Cucumber by Bingqing Xiao, Songxi Yuan, Ákos Bede‐Fazekas, Jinxin Zhou, Xingyu Song, Qiang Lin, Lei Cui, Zhixin Zhang

“…We first fitted SDMs for this species based on opportunistic occurrence records via four modeling algorithms, then built two types of ensemble models using stacked generalization: an ensemble model that solely used four model predictions and an expert‐informed ensemble model that further accounted for distance to the IUCN expert range map. …”
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Modeling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data Using FRK by Matthew Sainsbury-Dale, Andrew Zammit-Mangion, Noel Cressie

“…FRK is an R package for spatial and spatio-temporal modeling and prediction with very large data sets that, to date, has only supported linear process models and Gaussian data models. …”
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Deterministic Sea Wave Reconstruction and Prediction Based on Coherent S-Band Radar Using Condition Number Regularized Least Squares by Zhongqian Hu, Zezong Chen, Chen Zhao, Xi Chen

“…Coherent S-band radar is a remote sensing observation device with high spatial-temporal resolution and can be used to achieve deterministic sea wave reconstruction and prediction (DSWRP) technology. …”
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Spatial Modeling of Unemployment Rate in Counties of Iran Based on Population and Housing Census Data by Hamed Seifi, Abdollah Jalilian, Azad Khanzadi

“…In these data, the economically active population and the number of unemployed, aged 15 years old or above are categorized by gender and different levels of education in Iran counties. The aim pursued during this research is the spatial modeling of the number of unemployed in counties of Iran, based on gender and education as covariates. …”
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From Field to Model: Determining EROSION 3D Model Parameters for the Emerging Biomass Plant <i>Silphium perfoliatum</i> L. to Predict Effects on Water Erosion Processes by Tobias Koch, Peter Aartsma, Detlef Deumlich, Peter Chifflard, Kerstin Panten

“…The assessment of soil conservation measures requires calibrated soil erosion models that spatially identify soil erosion processes. …”
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Profiling user segments and spatial clusters of EV uptake through multi-method modeling by Bailing Zhang, Jing Kang

“…However, most existing approaches treat EV adoption as a homogeneous process or rely on fixed-rule models that overlook spatial clustering. This study implements a multi-method approach that combines probabilistic modeling and geospatial analysis to classify and profile EV and conventional vehicle (CV) users. …”
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Thermal dose feedback control systems applied to magnetic nanoparticle hyperthermia by Yash Sharad Lad, Shreeniket Pawar, Nageshwar Arepally, Hayden Carlton, Constantinos Hadjipanayis, Robert Ivkov, Ma’Moun Abu-Ayyad, Anilchandra Attaluri

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A Study of Tool Wear Prediction Based on Digital Twins by LIU Minghao, MAO Xinhui, XIA Wei, YUE Caixu, LIU Xianli

“…This model can deeply extract spatial features and dynamic temporal features, significantly improving prediction accuracy compared to conventional models. …”
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