Modeling and predicting of the spatial variations Precipitation cores in Iran by hossein naserzadeh, fariba sayadi, meysam toulabi nejad

“…The first type of data is the monthly precipitation of 86 synoptic stations with the statistical period of 1986-1989 and the second type of predicted data from the output of the CCSM4 model under the three scenarios (RCP2.6, RCP4.5, and RCP6) from 2016 to 2036. …”
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Spatial distribution prediction of pore pressure based on Mamba model by Xingye Liu, Xingye Liu, Bing Liu, Wenyue Wu, Qian Wang, Yuwei Liu

“…Advanced seismic inversion techniques are then employed to obtain three-dimensional elastic properties like subsurface velocity and density, which serve as input features for the trained deep learning model.ResultsThrough complex nonlinear mappings, the model effectively captures the intrinsic relationship between input attributes and formation pressure, enabling accurate spatial distribution prediction of formation pore pressure. …”
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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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Predicting the spatial distribution of water applied by subsurface drip in clay soil by Mayara O. Rocha, Amilton G. S. de Miranda, Policarpo A. da Silva, Adunias dos S. Teixeira, Fernando F. da Cunha

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The future of spatial epidemiology in the AI era: enhancing machine learning approaches with explicit spatial structure by Nima Kianfar, Benn Sartorius, Colleen L. Lau, Robert Bergquist, Behzad Kiani

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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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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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Model Predictive Control of Spatially Distributed Systems with Spatio-Temporal Logic Specifications by Ikkei Komizu, Koichi Kobayashi, Yuh Yamashita

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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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Subglacial hydrology modeling predicts high winter water pressure and spatially variable transmissivity at Helheim Glacier, Greenland by Aleah Sommers, Colin Meyer, Mathieu Morlighem, Harihar Rajaram, Kristin Poinar, Winnie Chu, Jessica Mejia

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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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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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Maximum entropy model-based spatial sinkhole occurrence prediction in Karapınar, Turkey by Fatih Sarı, Mustafa YALÇIN

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Introducing Spatial Heterogeneity via Regionalization Methods in Machine Learning Models for Geographical Prediction: A Spatially Conscious Paradigm by Lukas Boegl, Ourania Kounadi

“… This study addresses the challenge of incorporating spatial heterogeneity in predictive modeling by introducing regionalization methods in the preprocessing step of the modeling workflow. …”
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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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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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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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SWRVM: Sliding Window Recurrent Vision Mamba Model for Long-Term Spatial-Temporal Prediction by Ge Peng, Yujing Zhong

“…The data for these tasks typically exhibits multi-scale variability which imposes a great deal of difficulty for long-term prediction. A deep learning model, named sliding window recurrent vision mamba (SWRVM), is proposed for exploiting spatial and long-term temporal information accurately and dexterously to perform effective long-term spatial-temporal prediction in this paper. …”
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