Ionospheric Time Series Prediction Method Based on Spatio-Temporal Graph Neural Network by Yifei Chen, Yang Liu, Kunlin Yang, Lanhao Li, Chao Xiong, Jinling Wang

“…Predicting global ionospheric total electron content (TEC) is critical for high-precision GNSS applications, but some existing models fail to jointly capture spatial heterogeneity and multiscale temporal trends. …”
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Location, Location, Location: The Power of Neighborhoods for Apartment Price Predictions Based on Transaction Data by Christopher Kmen, Gerhard Navratil, Ioannis Giannopoulos

“…The best-performing models achieved an average MAPE of 15% for one-year-ahead predictions and maintained a MAPE below 20% for predictions up to three years ahead, demonstrating the effectiveness of leveraging spatial features to enhance real estate price prediction accuracy.…”
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Spatiotemporal prediction of alpine wetlands under multi-climate scenarios in the west of Sichuan, China by Haijun Wang, Xiangdong Kong, Onanong Phewnil, Ji Luo, Pengju Li, Xiyong Chen, Tianhui Xie

“…The thematic maps were then grid-sampled for predictive modeling of future wetland changes. Four species distribution models (SDMs), BIOCLIM, DOMAIN, MAXENT, and GARP were innovatively introduced. …”
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QSA-QConvLSTM: A Quantum Computing-Based Approach for Spatiotemporal Sequence Prediction by Wenbin Yu, Zongyuan Chen, Chengjun Zhang, Yadang Chen

“…The ability to capture long-distance dependencies is critical for improving the prediction accuracy of spatiotemporal prediction models. …”
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MHCAGAT: A Meta Hybrid Convolution Attention Network for Urban Traffic Flow Prediction by Yu Zhan, Suzi Iryanti Fadilah, Azizul Rahman Mohd Shariff

“…However, increasingly strict privacy regulations and highly fragmented data collection environments such as VANETs have substantially reduced the amount of usable data, thereby making it significantly more challenging to build accurate and reliable models. To address these issues, a novel traffic prediction model is proposed, Meta Hybrid Convolution Attention Graph Attention Network (MHCAGAT). …”
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A population spatialization method based on the integration of feature selection and an improved random forest model. by Zhen Zhao, Hongmei Guo, Xueli Jiang, Ying Zhang, Changjiang Lu, Can Zhang, Zonghang He

“…The random forest (RF) model is widely used in population spatialization studies. …”
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Estimativa da produção de sedimentos mediante uso de um modelo hidrossedimentológico acoplado a um SIG Sediment yield prediction through the use of a runoff-erosion model coupled t... by Richarde M. da Silva, Celso A. G. Santos

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Neural Field-Based Space Target 3D Reconstruction with Predicted Depth Priors by Tao Fu, Yu Zhou, Ying Wang, Jian Liu, Yamin Zhang, Qinglei Kong, Bo Chen

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Disease prevention versus data privacy: using landcover maps to inform spatial epidemic models. by Michael J Tildesley, Sadie J Ryan

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SASTGCN: Semantic-Augmented Spatio-temporal graph convolutional network for subway flow prediction by Shiyuan Jin, Changfeng Jing, Sheng Yao, Yushan Zhang, Pu Zhao, Jinlong Zhang

“…However, the existing work ignored the semantic similarity inherent in the subway stations function, which can extract passengers and enhance prediction accuracy. In this work, a Semantic-Augmented Spatio-temporal Graph Convolutional Network (SASTGCN) model was proposed, which considered semantic similarity, spatiotemporal correlations and spatial heterogeneity to realize the passenger inflow and outflow prediction. …”
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Assessing the Robustness of Multispectral Satellite Imagery with LiDAR Topographic Attributes and Ancillary Data to Predict Vertical Structure in a Wet Eucalypt Forest by Bechu K. V. Yadav, Arko Lucieer, Gregory J. Jordan, Susan C. Baker

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Graph-Based Prediction of Spatio-Temporal Vaccine Hesitancy From Insurance Claims Data by Sifat Afroj Moon, Rituparna Datta, Tanvir Ferdousi, Hannah Baek, Abhijin Adiga, Achla Marathe, Anil Vullikanti

“…The GNN uses a ZIP Code-level network to capture spatial signals from neighboring areas, while the RNN models the temporal dynamics present in the data. …”
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Temperature Prediction at Street Scale During a Heat Wave Using Random Forest by Panagiotis Gkirmpas, George Tsegas, Denise Boehnke, Christos Vlachokostas, Nicolas Moussiopoulos

“…Additionally, by using only the observed temperature as the target of the Random Forest model, higher accuracy is achieved, but spatial features are not represented in the predictions. …”
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The scrub typhus in mainland China: spatiotemporal expansion and risk prediction underpinned by complex factors by Hongwu Yao, Yixing Wang, Xianmiao Mi, Ye Sun, Kun Liu, Xinlou Li, Xiang Ren, Mengjie Geng, Yang Yang, Liping Wang, Wei Liu, Liqun Fang

“…A Cox proportional hazard model was used to identify drivers for spatial spread, and a boosted regression tree (BRT) model was constructed to predict potential risk areas. …”
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Noise robust aircraft trajectory prediction via autoregressive transformers with hybrid positional encoding by Youyou Li, Yuxiang Fang, Teng Long

“…Current trajectory prediction models often struggle in noisy scenarios due to their lack of robustness. …”
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Optimizing Crop Yield Prediction: An In-Depth Analysis of Outlier Detection Algorithms on Davangere Region by C. S. Anu, C. R. Nirmala, A. Bhowmik, A. Johnson Santhosh

“…Crop yield prediction is a critical aspect of agricultural planning and resource allocation, with outlier detection algorithms playing a vital role in refining the accuracy of predictive models. …”
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2D Spatiotemporal Hypergraph Convolution Network for Dynamic OD Traffic Flow Prediction by Cheng Fang, Li Wang

“…Experimental evaluation conducted on real-world datasets highlights the efficiency of our suggested model for predicting OD flows. Our results demonstrate a promising predictive performance, showcasing the ability of the 2D-HGCN to effectively capture the intricate dynamics of OD traffic flow.…”
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Enhancing prediction of fluid-saturated fracture characteristics using deep learning super resolution by Manju Pharkavi Murugesu, Vignesh Krishnan, Anthony R. Kovscek

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Predicting reproductive phenology of wind-pollinated trees via PlanetScope time series by Yiluan Song, Daniel S.W. Katz, Zhe Zhu, Claudie Beaulieu, Kai Zhu

“…Accurate airborne pollen concentration modeling and prediction rely on understanding plant reproductive phenology, particularly the timing of flowering and pollen release. …”
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Short-Term Passenger Flow Prediction Based on Federated Learning on the Urban Metro System by Guowen Dai, Jinjun Tang, Jie Zeng, Yuting Jiang

“…Accurate short-term metro passenger flow prediction is critical for urban transit management, yet existing methods face two key challenges: (1) privacy risks from centralized data collection and (2) limited capability to model spatiotemporal dependencies. …”
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