A framework for continual learning in real-time traffic forecasting utilizing spatial–temporal graph convolutional recurrent networks by Mariam Labib Francies, Abeer Twakol Khalil, Hanan M. Amer, Mohamed Maher Ata

“…To address these challenges, this research presents an innovative framework known as the Continual Learning-based Spatial–Temporal Graph Convolutional Recurrent Neural Network (STGNN-CL) for persistent and accurate long-term traffic flow prediction. …”
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Intelligent prediction method of virtual network function resource capacity for polymorphic network service slicing by Julong LAN, Di ZHU, Dan LI

“…First, the time series of data stream used for prediction is subjected to two-stage weighting processing,and then the processed time series and its dependent spatial topology information are input into the network model for spatiotemporal feature extraction. …”
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Nonlinear prediction model of vehicle network traffic management based on the internet of things by Zhijie Peng, Lili Yin

“…This research presents a novel nonlinear prediction model for Internet of Things (IoT) driven vehicle network traffic management. …”
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Integrated analysis of single-cell, spatial and bulk RNA-sequencing identifies a cell-death signature for predicting the outcomes of head and neck cancer by Yue Pan, Lei Fei, Shihua Wang, Hua Chen, Changqing Jiang, Hong Li, Changsong Wang, Yao Yang, Qinggao Zhang, Yongwen Chen

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PM2.5 prediction and its influencing factors in the Beijing-Tianjin-Hebei urban agglomeration using spatial temporal graph convolutional networks by Yawen Zhao

“…To address this, this study uses spatiotemporal analysis and Spatial Temporal Graph Convolutional Networks (ST-GCN) to evaluate the variation and driving factors of PM _2.5 concentrations in the Beijing-Tianjin-Hebei (BTH) urban agglomeration from 2014 to 2024, and to make predictions. …”
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Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies by Zhe Sun, Zhangxing Chen

“…This study systematically reviews advancements in formation damage prediction and diagnostics, focusing on wellsite diagnosis, experimental methods, imaging techniques, analytical approaches, numerical modeling, and artificial intelligence applications. …”
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CNN-based salient features in HSI image semantic target prediction by Vishal Srivastava, Bhaskar Biswas

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Expanding cryospheric landform inventories – quantitative approaches for underestimated periglacial block- and talus slopes in the Dry Andes of Argentina by Tamara Köhler, Anna Schoch-Baumann, Rainer Bell, Johannes Buckel, Diana Agostina Ortiz, Dario Trombotto Liaudat, Lothar Schrott

“…Random forest models produce robust and transferable predictions of both target landforms, demonstrating a high predictive power (mean AUROC values ≥0.95 using non-spatial validation and ≥0.83 using spatial validation). …”
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USING THE SLEUTH MODEL TO SIMULATE FUTURE URBAN GROWTH IN THE GREATER EASTERN ATTICA AREA, GREECE by Stylianos Mathioulakis, Yorgos N. Photis

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USING THE SLEUTH MODEL TO SIMULATE FUTURE URBAN GROWTH IN THE GREATER EASTERN ATTICA AREA, GREECE by Stylianos Mathioulakis, Yorgos N. Photis

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Research on a Crime Spatiotemporal Prediction Method Integrating Informer and ST-GCN: A Case Study of Four Crime Types in Chicago by Yuxiao Fan, Xiaofeng Hu, Jinming Hu

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Spatial analysis of annual precipitation of Khuzestan province; An approach of spatial regressions analysis by Saeed balyani

“…In this research, for determine of precipitation model and predicting of it with geographical factors e.g. altitude, slope and view shade and latitude- longitude by using spatial regressions analysis such as ordinary least squares (OLS) and geographical weighted regressions(GWR), 13 synoptic stations of Khuzestan province from establishment to 2010 were used. …”
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Spatial and temporal evolution of carbon stocks in Yulin City under changing environments by Guikai Sun, Yadong Li, Rui Huang, Chongxun Mo

“…It then applies the PLUS model to predict the land use of Yulin City under different scenarios in 2030 and forecasts the future carbon stock, providing a theoretical basis for the city’s future development planning. …”
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Evaluating Remote Sensing Resolutions and Machine Learning Methods for Biomass Yield Prediction in Northern Great Plains Pastures by Srinivasagan N. Subhashree, C. Igathinathane, John Hendrickson, David Archer, Mark Liebig, Jonathan Halvorson, Scott Kronberg, David Toledo, Kevin Sedivec

“…The developed methodology of RFE for feature selection and RF for biomass yield modeling is recommended for biomass and hay forage yield prediction.…”
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Multivariate Segment Expandable Encoder-Decoder Model for Time Series Forecasting by Yanhong Li, David C. Anastasiu

“…Additionally, MSEED incorporates a simple vanilla encoder-decoder model for strengthening rolling predictions. The framework has been tested on four challenging real-world datasets, focusing on two critical forecasting scenarios: long-term predictions (three days ahead) and rolling predictions (every four hours) to simulate real-time decision-making in water resource management. …”
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Enhancing Spatial Ability Assessment: Integrating Problem-Solving Strategies in Object Assembly Tasks Using Multimodal Joint-Hierarchical Cognitive Diagnosis Modeling by Jujia Li, Kaiwen Man, Joni M. Lakin

“…The MJ-DINA model consists of three sub-models: a Deterministic Inputs, Noisy “and” Gate (DINA) model for estimating spatial ability, a lognormal RT model for response time, and a Bayesian Negative Binomial (BNF) model for fixation counts. …”
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Enhancing proximal and remote sensing of soil organic carbon: A local modelling approach guided by spectral and spatial similarities by Qi Sun, Pu Shi

“…At large scale, increasing soil heterogeneity complicates the response relationship between soil spectra and SOC, making global models ineffective for local SOC predictions. Here, we propose a local learning approach that searches spectrally and spatially similar samples for site-specific SOC predictive modelling. …”
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Digital Twin Framework for Bridge Slab Deterioration: From 2D Inspection Data to Predictive 3D Maintenance Modeling by Hyunhye Song, Kiyeol Kim, Jihun Shin, Gitae Roh, Changsu Shim

“…Based on this data, eight representative damage states were defined to support the prediction of the service life. The damage and repair history was embedded into the 3D bridge models using a unique coding system to enable temporal and spatial tracking. …”
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Generative spatial artificial intelligence for sustainable smart cities: A pioneering large flow model for urban digital twin by Jeffrey Huang, Simon Elias Bibri, Paul Keel

“…The LFM demonstrates its novelty in comprehensive urban modeling and analysis by completing impartial city data, estimating flow data in new locations, predicting the evolution of flow data, and offering a holistic understanding of urban dynamics and their interconnections. …”
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Improved crop row detection by employing attention-based vision transformers and convolutional neural networks with integrated depth modeling for precise spatial accuracy by Hassan Afzaal, Derek Rude, Aitazaz A. Farooque, Gurjit S. Randhawa, Arnold W. Schumann, Nicholas Krouglicof

“…Incorporating artificial intelligence (AI) within agricultural practices has fundamentally transformed the discipline by facilitating sophisticated data analysis, predictive modeling, and automation. This research presents a novel framework that integrates deep learning, precision agriculture, and depth modeling to detect crop rows and spatial information accurately. …”
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