A Spatio-Temporal Tensor Graph Neural Network-Based Method for Node-Link Prediction in Port Networks by Zhixin Xia, Zhangqi Zheng, Feiyang Wei, Yongshan Liu, Lu Yu

“…Therefore, to effectively utilize the information of the dynamic network and improve the prediction efficiency as well as the prediction accuracy, this paper proposes a spatio-temporal tensor graph neural network model, which learns graph structural features from both spatial and temporal aspects to capture the evolution of the dynamic network. …”
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Modeling of spatial spread of COVID-19 pandemic waves in Russia using a kinetic-advection model by V. V. Aristov, A. V. Stroganov, A. D. Yastrebov

“…This paper studies the development of epidemic events in Russia, starting from the third and including the most recent fifth and sixth waves. Our twoparameter model is based on a kinetic equation. The investigated possibility of predicting the spatial spread of the virus according to the time lag of reaching the peak of infections in Russia as a whole as compared to Moscow is connected with geographical features: in Russia, as in some other countries, the main source of infection can be identified. …”
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Application of a land use regression (LUR) model to the spatial modelling of air pollutants in Esfahan city by Maryam Sharifi Sadeh, Mozhgan Ahmadi Nadoushan

“…Thus, LUR predicts the concentrations of pollution based on surrounding land use and traffic characteristics within circular areas (buffers) as predictors of measured concentrations. …”
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Spatiotemporal Pattern Analysis and Prediction of Carbon Storage Based on Land Use and Cover Change: A Case Study of Jiangsu Coastal Cities in China by Ge Shi, Yutong Wang, Jingran Zhang, Jinghai Xu, Yu Chen, Wei Chen, Jiahang Liu

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Leveraging Next‐Generation Satellite Remote Sensing‐Based Snow Data to Improve Seasonal Water Supply Predictions in a Practical Machine Learning‐Driven River Forecast System by Sean W. Fleming, Karl Rittger, Catalina M. Oaida Taglialatela, Indrani Graczyk

“…We test a new space‐based remote sensing product, spatially and temporally complete (STC) MODSCAG fractional snow‐covered area (fSCA), as input for the Natural Resources Conservation Service (NRCS) operational US West‐wide WSF system. fSCA data were considered alongside traditional SNOTEL predictors, in both statistical and AI‐based NRCS operational hydrologic models, throughout the forecast season, in four test watersheds (Walker, Wind, Piedra, and Gila Rivers in California, Wyoming, Colorado, and New Mexico). …”
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Advancement of a diagnostic prediction model for spatiotemporal calibration of earth observation data: a case study on projecting forest net primary production in the mid-latitude... by Eunbeen Park, Hyun-Woo Jo, Gregory Scott Biging, Jong Ahn Chun, Seong Woo Jeon, Yowhan Son, Florian Kraxner, Woo-Kyun Lee

“…This study introduced a diagnostic prediction concept as a generalized modeling framework for enhancing modeling precision and interpretability and demonstrate a case study of estimating forest net primary production (NPP) in a mid-latitude region (MLR) by developing a diagnostic NPP diagnostic prediction model (DNPM). …”
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Machine learning approach for 2D abrasion mapping in Sediment Bypass Tunnels: a case study of Koshibu SBT, Japan by Ahmed Emara, Sameh A. Kantoush, Mohamed Saber, Tetsuya Sumi, Vahid Nourani, Emad Mabrouk

“…Results indicate that the XGBoost model effectively predicts 2D spatial abrasions in SBTs, achieving an overall accuracy of 0.864, exceeding 0.9 in some sections. …”
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Modeling Spatial Distribution of Snow Water Equivalent Using Transfer Learning Across Mountainous Basins by Lama El Halabi, Utkarsh Mital, Dipankar Dwivedi

“…By conducting an exploratory factor analysis, we validated this hypothesis and refined our TL model, which incorporated data based on 80 snowpack maps from California to predict SWE in Colorado. …”
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Spatial Modeling of Yellowfin Tuna in the Banda Sea Based on Oceanographic Factors Using MaxEnt by Sunarwan Asuhadi, Mukti Zainuddin, Safruddin Safruddin, Musbir Musbir

“…This study models the spatial distribution of yellowfin tuna (YFT) in the Banda Sea using the MaxEnt approach, addressing critical questions about its predictive capability, the influence of environmental variables such as sea surface temperature (SST) and chlorophyll-a concentration, and temporal patterns. …”
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Modeling the spatial distribution of African buffalo (Syncerus caffer) in the Kruger National Park, South Africa. by Kristen Hughes, Geoffrey T Fosgate, Christine M Budke, Michael P Ward, Ruth Kerry, Ben Ingram

“…Spatial distribution models were created using buffalo census information and archived data from previous research. …”
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Ensemble Learning for Spatial Modeling of Icing Fields from Multi-Source Remote Sensing Data by Shaohui Zhou, Zhiqiu Gao, Bo Gong, Hourong Zhang, Haipeng Zhang, Jinqiang He, Xingya Xi

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The impact of the subventricular zone invasion types and MGMT methylation status on tumor recurrence and prognosis in glioblastoma by Zhiying Shao, Hao Yan, Min Zhu, Zhengyang Liu, Ziqin Chen, Weiqi Li, Chenyang Wang, Longzhen Zhang, Junnian Zheng

“…We aimed to conduct a retrospective study to mainly investigate the prognostic value of SVZ invasion and MGMT status, and developed a novel clinical prediction model based on our findings. Methods: 139 patients with IDH wild-type GBM were retrospectively studied. …”
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Comparison of spatial dynamics and point kinetics approaches in multiphysics modeling of the molten salt reactor experiment by Philip Pfahl, Mustafa K. Jaradat, Mauricio E. Tano, Ramiro O. Freile, Samuel A. Walker, Javier Ortensi

“…The 0-D code Squirrel accurately predicted the time-dependent behavior in the MSRE given the steady-state spatial dynamics solution of Griffin.…”
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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

“…The primary objective is to evaluate how incorporating spatially correlated random properties can enhance the accuracy of predictions in mining operations. …”
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Spatially varying parameters improve carbon cycle modeling in the Amazon rainforest with ORCHIDEE r8849 by L. Zhu, L. Zhu, L. Zhu, L. Zhu, P. Ciais, Y. Yao, D. Goll, S. Luyssaert, I. Martínez Cano, A. Fendrich, A. Fendrich, L. Li, H. Yang, S. Saatchi, W. Li, W. Li

“…<p>Uncertainty in the dynamics of the Amazon rainforest poses a critical challenge for accurately modeling the global carbon cycle. Current dynamic global vegetation models (DGVMs), which use one or two plant functional types for tropical rainforests, fail to capture observed biomass and mortality gradients in this region, raising concerns about their ability to predict forest responses to global change drivers. …”
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Machine Learning-Enhanced 3D GIS Urban Noise Mapping with Multi-Modal Factors by Jianping Pan, Yuzhe He, Wei Ma, Shengwang An, Lu Li, Dan Huang, Dunxin Jia

“…Most existing noise prediction models fail to fully account for three-dimensional (3D) spatial information and a wide range of environmental factors. …”
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Six-Dimensional Spatial Dimension Chain Modeling via Transfer Matrix Method with Coupled Form Error Distributions by Lu Liu, Xin Jin, Huan Guo, Chaojiang Li

“…The experimental validation on an aero-engine casing assembly shows that the SDC model captures multidimensional closed-loop spatial errors, with absolute errors of max–min closed-loop distances below 9.3 μm and coaxiality prediction errors under 8.3%. …”
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Vehicle trajectory prediction based on spatio-temporal Transformer feature fusion by ZHAO Wenhong, WANG Wei, WAN Zilu

“…The framework initially employs a spatial self-attention mechanism to capture the spatial interactions between vehicles at the same moment, achieving precise modeling of the spatial relationship interactivity among multiple vehicles. …”
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Optimizing fully-efficient two-stage models for genomic selection using open-source software by Javier Fernández-González, Julio Isidro y Sánchez

“…Two-stage models, preferred for their simplicity and efficiency, first calculate adjusted genotypic means accounting for spatial variation within each environment, then use these means to predict GEBVs. …”
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SIAT: Pedestrian trajectory prediction via social interaction-aware transformer by Chengdong Wang, Jianming Wang, Wenbo Gao, Lei Guo

“…The novel model framework establishes a new benchmark for mixed models in trajectory prediction.…”
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