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

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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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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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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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