The spatial risk of cyclone wave damage across the Great Barrier Reef by Mandy W.M. Cheung, Milani Chaloupka, Peter J. Mumby, David P. Callaghan

“…We then applied a statistical model with likelihood inference to predict damage given cyclone strength and reef spatial arrangement, and calibrated the model using field observations from five cyclones. …”
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Graph neural network driven traffic prediction technology:review and challenge by Yi ZHOU, Shuting HU, Wei LI, Nan CHENG, Ning LU, Xuemin(Sherman) SHEN

“…With the rapid development of Internet of things and artificial intelligence technology, accurate analysis and prediction of traffic data have become the primary target of intelligent transportations.In recent years, the method of traffic forecasting has gradually changed from the classical model-driven type to the data-driven type.However, how to effectively analyze the spatial-temporal characteristics of road networks through big data is one of the key issues in the traffic prediction process.Spatiotemporal big data analysis is a powerful tool for the traffic prediction.The traffic network can be modeled as a graph network, while the deep learning method can be extended on the graph network.Utilizing graph neural networks, we can build the spatiotemporal prediction model, and obtain the spatial-temporal correlation between the sensor nodes in road networks effectively by using graph convolution, which can significantly improve the accuracy of traffic prediction models.The traffic forecasting technology driven by graph neural networks was explored, and two kinds of traffic prediction models based on the analysis of deep spatial-temporal characteristics were extracted.The actual cases were analyzed and evaluated to discuss the technical advantages and key challenges of graph neural networks in the traffic prediction.The potential issues of graph neural network driven prediction mechanisms were also excavated.…”
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Graph neural network driven traffic prediction technology:review and challenge by Yi ZHOU, Shuting HU, Wei LI, Nan CHENG, Ning LU, Xuemin(Sherman) SHEN

“…With the rapid development of Internet of things and artificial intelligence technology, accurate analysis and prediction of traffic data have become the primary target of intelligent transportations.In recent years, the method of traffic forecasting has gradually changed from the classical model-driven type to the data-driven type.However, how to effectively analyze the spatial-temporal characteristics of road networks through big data is one of the key issues in the traffic prediction process.Spatiotemporal big data analysis is a powerful tool for the traffic prediction.The traffic network can be modeled as a graph network, while the deep learning method can be extended on the graph network.Utilizing graph neural networks, we can build the spatiotemporal prediction model, and obtain the spatial-temporal correlation between the sensor nodes in road networks effectively by using graph convolution, which can significantly improve the accuracy of traffic prediction models.The traffic forecasting technology driven by graph neural networks was explored, and two kinds of traffic prediction models based on the analysis of deep spatial-temporal characteristics were extracted.The actual cases were analyzed and evaluated to discuss the technical advantages and key challenges of graph neural networks in the traffic prediction.The potential issues of graph neural network driven prediction mechanisms were also excavated.…”
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Development of a Predictive Model for N-Dealkylation of Amine Contaminants Based on Machine Learning Methods by Shiyang Cheng, Qihang Zhang, Hao Min, Wenhui Jiang, Jueting Liu, Chunsheng Liu, Zehua Wang

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MODIFIED WEIGHT MATRIX USING PRIM’S ALGORITHM IN MINIMUM SPANNING TREE (MST) APROACH FOR GSTAR(1;1) MODEL by Nur'ainul Miftahul Huda, Fransiskus Fran, Yundari Yundari, Lisa Fikadila, Fauziah Safitri

“…The existence of a weight matrix is one of the aspects that established this model. The matrix illustrates the spatial impact that occurs between locations. …”
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A spatially explicit containment modelling approach for escaped wildfires in a Mediterranean climate using machine learning by Gbenga Lawrence Alawode, Pere Joan Gelabert, Marcos Rodrigues

“…Effectively suppressing large wildfires requires a thorough understanding of containment opportunities across landscapes, to which empirical spatial modelling can contribute largely. The previous containment model in Catalonia failed to account for the crucial roles of weather conditions, lacked temporal prediction and could not forecast windows for containment opportunities, prompting this research. …”
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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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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 Dust Storms in Iran based on Climatic and Vegetation Characteristics by Farzaneh Borzou, Hasan Zolfaghari, Jafar Masoompour Samakosh, Jalil Sahraei

“…The present study aims to identify and spatially analyze the sources of Iran dust storms by using the National Centers for Environmental Prediction (NCEP/DOE); European Centre for Medium-Range Weather Forecasts operational (ECMWF) ERA-Interim reanalysis datasets and the records of 52 synoptic stations from 1984 to 2016. …”
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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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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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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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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

“…We finally used the Zero-Inflated Poisson (ZIP) regression model to predict tsetse abundance due to its superiority over the standard Poisson after model fitting and testing using the Vuong Non-Nested statistic.…”
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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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