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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Preserving Spatial Patterns in Point Data: A Generalization Approach Using Agent-Based Modeling by Martin Knura, Jochen Schiewe

“…We present the architecture of the model and compare the results with methods focusing on extreme value preservation as well as clutter reduction. …”
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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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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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Hydrologic performance assessment of low impact development facilities based on monitoring data and SWMM modeling in an urban catchment in Taiwan by Yu-Jia Chiu, Lo-Chen Chang, Yu-Te Lin, Ying-Tien Lin, Chen-Wuing Liu, Jin-Jing Lee

“…Simulations performed using the Storm Water Management Model (SWMM) calibrated using field data collected in 2021–2024 demonstrated that these LID facilities reduced runoff volume by as much as 88%, with peak flow reductions reaching 90%. …”
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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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Prediction of Urban Construction Land Carbon Effects (UCLCE) Using BP Neural Network Model: A Case Study of Changxing, Zhejiang Province, China by Qinghua Liao, Xiaoping Zhang, Zixuan Cui, Xunxi Yin

“…The results demonstrate that the BP neural network model effectively predicts the different types of UCLCE, with an average error rate of 30.10%. (1) The total effect and intensity effect exhibit different trends in the study area, and a carbon effect table for different types of UCL is established. (2) The spatial distribution characteristics of UCLCE reveal a distinct reverse-L pattern (“┙”-shaped layout) with positive spatial correlation (Moran’s I = 0.11, <i>p</i> < 0.001). (3) The model’s core practical value lies in enabling forward-looking assessment of carbon effects in urban planning schemes and precise quantification of emissions reduction benefits. …”
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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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Modeling consequences of spatial closures for offshore energy: Loss of fishing grounds and fishery‐independent data by M. Campbell, J. F. Samhouri, J. W. White

“…This produced two effects in the model: initial loss of fishery yield due to the closure and reductions in fishing effort when the loss of data triggered precautionary measures in the harvest control rule. …”
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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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Understanding the impact of covariates for trachoma prevalence prediction using geostatistical methods by Misaki Sasanami, Ibrahim Almou, Adam Nouhou Diori, Ana Bakhtiari, Nassirou Beidou, Donal Bisanzio, Sarah Boyd, Clara R. Burgert-Brucker, Abdou Amza, Katherine Gass, Boubacar Kadri, Fikreab Kebede, Michael P. Masika, Nicholas P. Olobio, Fikre Seife, Abdoul Salam Youssoufou Souley, Amsayaw Tefera, Amir B. Kello, Anthony W. Solomon, Emma M. Harding-Esch, Emanuele Giorgi

“…For each fitted model, we reported estimates of the parameters that quantify the strength of residual spatial correlation and 95% prediction intervals as the measure of uncertainty. …”
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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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In situ and dynamic screening of extracellular vesicles as predictive biomarkers in immune-checkpoint inhibitor therapies by Yihe Wang, Yue Sun, Mengqi Liu, Chao Wang, Miao Huang, Jiaoyan Qiu, Ningkai Yang, Yu Zhang, Hong Liu, Lin Han

“…This platform enables in situ monitoring of EV secretion dynamics under ICI and chemotherapeutic treatments, capturing localized and temporal changes in EV release. Using predictive models, we identified EVs carrying programmed cell death ligand 1 (PD-L1) as the most robust predictors of spheroid viability during treatment. …”
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