Spatial correlation effects on rock mass behavior: insights from stochastic modeling in longwall mining by Mohammad Reza Soleimanfar, Reza Shirinabadi, Navid Hosseini Alaee, Ehsan Moosavi, Ghodratollah Mohammadi

“…Abstract The mechanical behavior of rock masses in longwall mining is critically influenced by spatial correlation among material properties, yet conventional deterministic models often overlook this variability. …”
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A data-driven spatial-temporal model for prediction of tunnel deformation by Ziyi Zhang, Han Zhang, Cong Du, Mingzhao Wei, Xiaochao Wang, Jianqing Wu

“…Due to the deficiency of incomplete influencing factors and rough prediction accuracy, this paper proposes a data-driven spatial-temporal model to predict tunnel deformation behavior. …”
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Point Transformer Network-Based Surrogate Model for Spatial Prediction in Bridges by Javier Grandío, Brais Barros, Manuel Cabaleiro, Belén Riveiro

“…To overcome this computational limitation, this paper presents an innovative deep learning-based surrogate model for predicting local displacements in bridge structures. …”
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Comparative Analysis of The Combined Model (Spatial and Temporal) and Regression Models for Predicting Murder Crime by Laith S. Ibrahim, Ghadeer Jasim Mohammed

“… This research dealt with the analysis of murder crime data in Iraq in its temporal and spatial dimensions, then it focused on building a new model with an algorithm that combines the characteristics associated with time and spatial series so that this model can predict more accurately than other models by comparing them with this model, which we called the Combined Regression model (CR), which consists of merging two models, the time series regression model with the spatial regression model, and making them one model that can analyze data in its temporal and spatial dimensions. …”
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How spatial scales enhance prediction: an interpretable multi-scale framework for bike-sharing demand prediction by Jiasong Zhu, Jingbiao Chen, Mingxiao Li, Wei Tu

“…However, most studies focus only on one specific spatial scale, thus ignoring the inter-scale synergy improvement on prediction performance. …”
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Spatial differences in predicted Phalaris arundinacea (reed canarygrass) occurrence in floodplain forest understories by John T. Delaney, M. Van Appledorn, N. R. De Jager, K. L. Bouska, J. J. Rohweder

“…The ensemble of the three models (i.e., the average prediction) was used to map and summarize potential reed canary grass habitat suitability across the landscape. …”
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Spatially explicit predictions using spatial eigenvector maps by Guillaume Guénard, Pierre Legendre

“…Abstract In this paper, we explain how to obtain sets of descriptors of the spatial variation, which we call “predictive Moran's eigenvector maps” (pMEM), that can be used to make spatially explicit predictions for any environmental variables, biotic or abiotic. …”
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Predictive modeling of building energy consumption and thermal comfort for decarbonization in construction and retrofitting by Sameer Algburi, Aymen Mohammed, Ibrahim Abdullah, Talib Munshid Hanoon, Hassan Falah Fakhruldeen, Otabek Mukhitdinov, Feryal Ibrahim Jabbar, Qusay Hassan, Ali Khudhair, David Kato

“…This study introduces an integrated predictive modeling framework for assessing building energy consumption and indoor thermal comfort, with a focus on supporting decarbonization efforts in both new construction and retrofit scenarios. …”
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Addressing spatial imprecision in deep learning for satellite imagery-based socioeconomic predictions by Heather Baier, Dan Runfola

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Modelling and predicting biogeographical patterns in river networks by Sabela Lois

“…I show that biomass and abundance of host fish are a likely explanation for the autocorrelation in mussel abundance within a 15-km spatial extent. The application of universal kriging with the empirical model enabled precise prediction of mussel abundance within segments of river networks, something that has the potential to inform conservation biogeography. …”
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Characterizing US Spatial Connectivity and Implications for Geographical Disease Dynamics and Metapopulation Modeling: Longitudinal Observational Study by Giulia Pullano, Lucila Gisele Alvarez-Zuzek, Vittoria Colizza, Shweta Bansal

“…ObjectiveThis study aimed to address the questions that are critical for developing accurate transmission models, predicting the spatial propagation of disease across scales, and understanding the optimal geographical and temporal scale for the implementation of control policies. …”
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Introducing spatial information into predictive NF-kappaB modelling--an agent-based approach. by Mark Pogson, Mike Holcombe, Rod Smallwood, Eva Qwarnstrom

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SWRVM: Sliding Window Recurrent Vision Mamba Model for Long-Term Spatial-Temporal Prediction by Ge Peng, Yujing Zhong

“…The quantitative and qualitative visualization results demonstrate the SWRVM model outperforms the state-of-the-arts (SOTA) models in multi-scale variations long-term spatial-temporal prediction tasks.…”
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AGCN-T: A Traffic Flow Prediction Model for Spatial-Temporal Network Dynamics by Jian Feng, Lang Yu, Rui Ma

“…Aiming at the lack of the ability to model complex and dynamic spatial-temporal dependencies in current research, this paper proposes a traffic flow prediction model Attention based Graph Convolution Network (GCN) and Transformer (AGCN-T) to model spatial-temporal network dynamics of traffic flow, which can extract dynamic spatial dependence and long-distance temporal dependence to improve the accuracy of multistep traffic prediction. …”
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Comparison of spatial prediction models from Machine Learning of cholangiocarcinoma incidence in Thailand by Oraya Sahat, Supot Kamsa-ard, Apiradee Lim, Siriporn Kamsa-ard, Matias Garcia-Constantino, Idongesit Ekerete

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A Surrogate Model for the Rapid Prediction of Factor of Safety in Slopes with Spatial Variability by Xitailang Cao, Shan Lin, Miao Dong, Quanke Hu, Hong Zheng

“…To address this issue, this study proposes an efficient surrogate modeling approach for the rapid prediction of the factor of safety in slopes while considering the spatial variability of geotechnical parameters. …”
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Maximum entropy model-based spatial sinkhole occurrence prediction in Karapınar, Turkey by Fatih Sarı, Mustafa YALÇIN

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Impact of data spatial resolution on barley yield prediction mapping by F. Ksantini, M. Quemada, N. Arencibia-Pérez, A.F. Almeida-Ñauñay, E. Sanz, Ana M. Tarquis

“…The choice of spatial resolution plays a key role in model performance, as the resolution of data input influences prediction quality. …”
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Predict the carcinogenicity of compounds with SGCN by Wei Ruobing, He Jiafeng, Qiu Xiaofang, Liu Qi

“…In this paper, 341 kinds of experimental data were obtained, and the spatial atom feature combined with the spatial graph convolutional network(SGCN) was used to establish a model that could predict the carcinogenicity of compounds. …”
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Spatial Variables and Land Use Change Models: A Study on Conditioning Patterns of Natural Vegetation Suppression and Persistence by Macleidi Varnier, Eliseu José Weber

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