Topographic Position Index Predicts Within-Field Yield Variation in a Dryland Cereal Production System by Jacob A. Macdonald, David M. Barnard, Kyle R. Mankin, Grace L. Miner, Robert H. Erskine, David J. Poss, Sushant Mehan, Adam L. Mahood, Maysoon M. Mikha

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A Systematic Literature Review on the Application of Machine Learning for Predicting Stunting Prevalence in Indonesia (2020–2024) by Emilda Indrisari, Hidayat Febiansyah, Bambang Adiwinoto

“…This study recommends future research to focus on integrating spatial-temporal data, implementing Explainable AI (XAI), and conducting cross-regional validation to enhance model reliability and policy relevance.…”
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Information-Theoretic Modeling of Categorical Spatiotemporal GIS Data by David Percy, Martin Zwick

“…An NLCD tool reports how much change occurred for each category of land use; for the study area examined, the most dynamic class is Evergreen Forest (EFO), so the presence or absence of EFO in 2021 was chosen as the dependent variable that our data modeling attempts to predict. RA predicts the outcome with approximately 80% accuracy using a sparse set of cells from a spacetime data cube consisting of neighboring lagged-time cells. …”
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Mapping the covariate-adjusted spatial effects of childhood anemia in Ethiopia using a semi-parametric additive model by Seyifemickael Amare Yilema, Seyifemickael Amare Yilema, Yegnanew A. Shiferaw, Najmeh Nakhaeirad, Ding-Geng Chen, Ding-Geng Chen

“…Each predictor variable was spatially adjusted using non-parametric smoothing techniques based on geolocation parameters, and corresponding maps for each predictor.ResultsA regularized random forest techniques was employed to identify the most influential predictors of childhood anemia and enhance the model predictive performance. …”
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Spatial analysis of G.f.fuscipes abundance in Uganda using Poisson and Zero-Inflated Poisson regression models. by Albert Mugenyi, Dennis Muhanguzi, Guy Hendrickx, Gaëlle Nicolas, Charles Waiswa, Steve Torr, Susan Christina Welburn, Peter M Atkinson

“…In a bid to overcome these limitations, this study attempted to explore modelling skills that can be applied to spatially estimate tsetse abundance in the country using limited tsetse data and a set of remote-sensed environmental variables.…”
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Method for prediction magnetic silencing of uncertain energy-saturated extended technical objects in prolate spheroidal coordinate system by B. I. Kuznetsov, A. S. Kutsenko, T. B. Nikitina, I. V. Bovdui, K. V. Chunikhin, V. V. Kolomiets

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A meta-learning approach to improving transferability for freeway traffic crash risk prediction by Chenlei Liao, Xiqun (Michael) Chen

“…Due to the limited availability of crash data in some freeway sections, model transferability of crash risk prediction has become an essential topic in traffic safety research. …”
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Prediction of polar motion and UT1-UTC based on the hybrid EEMD_LSTM model by Chenxiang Wang, Pengfei Zhang, Wenbin Shen, Jiayao Wang

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A Criterion of Reduction Quality of Femoral Neck Fractures Based on Spatial Residual Displacements of Femoral Head by Shuang‐Shuang Cui, Li‐Kun Zhao, Jing‐Bo Yu, Jian‐Xiong Ma, Xin‐Long Ma

“…The purpose of this study is to construct a reduction quality criterion based on spatial residual displacements of 3D reconstruction and to predict the occurrence of ONFH. …”
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Impact of wildfires on spatial and temporal evolution of groundwater recharge in an Atlantic pine forest: An integrated approach using field, remote sensing and modeling. by Mariana La Pasta Cordeiro, João Pedro Nunes, Maria Teresa Condesso de Melo

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GEOGRAPHICALLY WEIGHTED MACHINE LEARNING MODEL FOR ADDRESSING SPATIAL HETEROGENEITY OF PUBLIC HEALTH DEVELOPMENT INDEX IN JAVA ISLAND by Muhammad Azis Suprayogi, Bagus Sartono, Khairil Anwar Notodiputro

“…Our results show that the non-parametric GW-RF model shows high potential for explaining spatial heterogeneity and predicting PHDI versus a global model when including six major risk factors. …”
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Investigating the impacts of telecommuting on the spatial, temporal, and modal distribution of travel using an agent-based transport simulation model by Bijoy Saha, Mahmudur Rahman Fatmi, Nazmul Arefin Khan

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Highly Sensitive Photoacoustic Imaging: A New Strategy for Ultrahigh Spatial Resolution Seismic Physical Model Imaging by Xin Liu, Wen Wang, Qiangzhou Rong, Baozhi Yu

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Phase field modeling for fracture prediction in goat tibia using an open-source quantitative computer tomography based finite element framework by Debangshu Paul, Zachariah Arwood, Pierre-Yves Mulon, Dayakar Penumadu, Timothy Truster

“…While predicting mechanical responses under various stress scenarios is of significant interest in the field of orthopedic research, finite element (FE) modeling studies specifically focusing on the tibia remain notably limited. …”
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Statistical damage constitutive model of cemented sand and gravelbased on a modified spatially mobilized plane yield criterion by Jiangui Yang, Honglei Ren, Qiang Li, Shuling Cai

“…The physical significance of the WeibullWeibull distribution parameters m and F0 was examined, and the variation of these parameters with respect to gel content and confining pressure was analyzed and modified. Finally, the predicted results of the proposed model were compared with experimental values and those from other constitutive models. …”
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Simulating Land Use and Evaluating Spatial Patterns in Wuhan Under Multiple Climate Scenarios: An Integrated SD-PLUS-FD Modeling Approach by Hao Yuan, Xinyu Li, Meichen Ding, Guoqiang Shen, Mengyuan Xu

“…Wuhan is selected as the case study area, with simulations conducted under three IPCC-aligned climate scenarios—SSP1-2.6, SSP2-4.5, and SSP5-8.5—to project land use changes by 2030. The SD model demonstrates robust predictive performance, with an overall error of less than ±5%, while the PLUS model achieves high spatial accuracy (average Kappa >0.7996; average overall accuracy >0.8856). …”
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Predictive Modeling the Turbidity Response in Al-Saray Water Distribution Network in Najaf Governorate/Middle of Iraq, Using PODDS Model by Abed Zahraa H., Jasem Hayder M., Mohammed Hayder S.

“…Reducing water turbidity is one of the main issues the water industry is currently experiencing. The ability to predict the spatial probability and intensity of discoloration events in distribution systems can lead to the adoption and improvement of proactive operation and maintenance strategies to reduce turbidity. …”
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Influence of the Human Skin Tumor Type in Photodynamic Therapy Analysed by a Predictive Model by I. Salas-García, F. Fanjul-Vélez, J. L. Arce-Diego

“…We employ a predictive PDT model and apply it to different skin tumors. …”
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Multiscale Spatio-Temporal Attention Network for Sea Surface Temperature Prediction by Zhenxiang Bai, Zhengya Sun, Bojie Fan, An-An Liu, Zhiqiang Wei, Bo Yin

“…Deep learning has shown preliminary success in modeling the dynamic spatial-temporal dependencies within SST signals, yet it remains challenging to obtain precise SSTs due to the inherent variabilities across multiple temporal and spatial scales, driven by distinct physical processes. …”
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Quantifying Root Cohesion Spatial Heterogeneity Using Remote Sensing for Improved Landslide Susceptibility Modeling: A Case Study of Caijiachuan Landslides by Zelang Miao, Yaopeng Xiong, Zhiwei Cheng, Bin Wu, Wei Wang, Zuwu Peng

“…This approach, integrated with land use-specific hydrological parameters and an infinite slope stability model, significantly improves landslide susceptibility predictions compared to models ignoring root cohesion or using uniform assignments. …”
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