SolarGAN for Meso-Level Solar Radiation Prediction at the Urban Scale: A Case Study in Boston by Yijun Lu, Xinru Li, Siyuan Wu, Yuankai Wang, Waishan Qiu, Da Chen, Yifan Li

“…This study introduces a method for predicting urban solar radiation using 2D mapping data, applying a Generative Adversarial Network (GAN) model to the city of Boston. …”
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SAM-Net: Spatio-Temporal Sequence Typhoon Cloud Image Prediction Net with Self-Attention Memory by Yanzhao Ren, Jinyuan Ye, Xiaochuan Wang, Fengjin Xiao, Ruijun Liu

“…In this process, the changes in time and space are crucial for spatio-temporal sequence prediction models. However, most models now rely on stacking convolutional layers to obtain local spatial features. …”
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Dynamic prediction and recommendation of museum visitors' interest based on long short-term memory network (LSTM) by Sha Nie

“…DALIR achieves a 15.9% improvement in visit sequence prediction accuracy and a 16.6% improvement in dwell time prediction accuracy over baseline models (TMSNN, GCN, LDA-LSTM). …”
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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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A Generalized GNN-Transformer-Based Radio Link Failure Prediction Framework in 5G RAN by Kazi Hasan, Khaleda Papry, Thomas Trappenberg, Israat Haque

“…Usually, historical radio link Key Performance Indicators (KPIs) and their surrounding weather station observations are utilized for building learning-based RLF prediction models. However, such models must be capable of learning the spatial weather context in a dynamic RAN and effectively encoding time series KPIs with the weather observation data. …”
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Spatiotemporal Flood Hazard Classification in Bangkok Using Graph Convolutional Network and Temporal Fusion Transformer by Pakpoom Chaimook, Nirattaya Khamsemanan, Cholwich Nattee, Alice Sharp

“…High population density, low elevation, and seasonal monsoons contribute to increased vulnerability to flooding. Traditional flood prediction models often fail to capture spatial correlations across districts and the temporal patterns within different types of features. …”
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OKG-ConvGRU: A Domain Knowledge-Guided Remote Sensing Prediction Framework for Ocean Elements by Renhao Xiao, Yixiang Chen, Lizhi Miao, Jie Jiang, Donglin Zhang, Zhou Su

“…Existing spatio-temporal prediction models primarily rely on either physical or data-driven approaches. …”
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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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A Data-Driven Approach for Urban Heat Island Predictions: Rethinking the Evaluation Metrics and Data Preprocessing by Berk Kıvılcım, Patrick Erik Bradley

“…The trained models with Random Forest and XGBoost methods which are capable of predicting the spatial distribution of air temperature by using building volume information are compared. …”
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Study on Key Influencing Factors of Carbon Emissions from Farmland Resource Utilization in Northeast China Under the Background of Energy Conservation and Emission Reduction by Mulin Sun, Yuhao Fu, Mingyao Sun, Run Huang, Yun Teng

“…A gray prediction model is constructed to predict the carbon emissions from the utilization of farmland resources in the next 10 years, and the logarithmic mean Divisia index model is used to analyze the effects of the various influencing factors on the carbon emissions from farmland utilization. …”
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Heterogeneity and distribution characteristics of tertiary lymphoid structures predict prognostic outcome in esophageal squamous cell carcinoma by Chengjuan Zhang, Ruihua Bai, Yanping Hu, Tao Wang, Bin Ma, Junxia Zhang, Jing Yuan, Xiance Tang, He Zhang, Tingjie Wang, Yuxi Chang, Qingxin Xia, Bing Wei

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Coal burst spatio-temporal prediction method based on bidirectional long short-term memory network by Xu Yang, Yapeng Liu, Anye Cao, Yaoqi Liu, Changbin Wang, Weiwei Zhao, Qiang Niu

“…The method involves three main modules, including microseismic spatio-temporal characteristic indicators construction, temporal prediction model, and spatial prediction model. To validate the effectiveness of the proposed method, engineering application tests are conducted at a high-risk working face in the Ordos mining area of Inner Mongolia, focusing on 13 high-energy microseismic events with energy levels greater than 105 J. …”
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TSTA-GCN: trend spatio-temporal traffic flow prediction using adaptive graph convolution network by Xinlu Zong, Jiawei Guo, Fucai Liu, Fan Yu

“…Abstract Balancing the need to satisfy both long-term and short-term requirements and comprehensively considering spatial and temporal dependencies are key challenges in metro passenger prediction. …”
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BiDGCNLLM: A Graph–Language Model for Drone State Forecasting and Separation in Urban Air Mobility Using Digital Twin-Augmented Remote ID Data by Zhang Wen, Junjie Zhao, An Zhang, Wenhao Bi, Boyu Kuang, Yu Su, Ruixin Wang

“…Using Remote ID data, we propose BiDGCNLLM, a hybrid prediction framework that integrates a Bidirectional Graph Convolutional Network (BiGCN) with Dynamic Edge Weighting and a reprogrammed Large Language Model (LLM, Qwen2.5–0.5B) to capture spatial dependencies and temporal patterns in drone speed trajectories. …”
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Assessment of Tumor Infiltrating Lymphocytes in Predicting Stereotactic Ablative Radiotherapy (SABR) Response in Unresectable Breast Cancer by Mateusz Bielecki, Khadijeh Saednia, Fang-I Lu, Shely Kagan, Danny Vesprini, Katarzyna J. Jerzak, Roberto Salgado, Raffi Karshafian, William T. Tran

“…Conclusions: Spatial TIL models demonstrate strong potential for predicting SABR response in inoperable breast cancer. …”
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Agricultural GDP exposure to drought and its machine learning-based prediction in the Jialing River Basin, China by Xinzhi Wang, Qingxia Lin, Zhiyong Wu, Yuliang Zhang, Changwen Li, Ji Liu, Shinan Zhang, Songyu Li

“…Cropland has shifted from higher exposure to long-term drought to higher exposure to short-term, frequency drought. (3) Among the four machine learning models, the Bayesian model demonstrated superior performance in precipitation and temperature predictions, respectively, while the BiGRU model exhibited the best performance in long-term predictions of evaporation and soil moisture. (4) The central and southern regions will further increase in agricultural GDP exposure to both meteorological and agricultural droughts from 2021 to 2030, with exposures anticipated to increase by 20.2–34.8 % compared to the period from 2011 to 2020. …”
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Enhanced wheat yield prediction through integrated climate and satellite data using advanced AI techniques by Muhamad Ashfaq, Imran Khan, Rana Fezan Afzal, Dilawar Shah, Shujaat Ali, Muhammad Tahir

“…Among the tested models, those capable of capturing spatial and temporal patterns reduced prediction errors most effectively. …”
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CT-based quantification of spatiotemporal heterogeneity for predicting response to neoadjuvant chemotherapy in locally advanced gastric cancer by Wenjun Hu, Liangjie Lin, Changjun Ma, Anliang Chen, Zhongbao Luo, Ziming Zhang, Ailian Liu

“…Abstract Objective To explore the efficacy of quantitative measurements of temporal changes in CT-based spatial habitat to predict pathological responses after neoadjuvant chemotherapy (NAC) in patients with locally advanced gastric cancer (LAGC). …”
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Predicting groundwater withdrawals using machine learning with limited metering data: Assessment of training data requirements by Dawit Asfaw, Ryan G. Smith, Sayantan Majumdar, Katherine Grote, Bin Fang, B.B. Wilson, V. Lakshmi, J.J. Butler, Jr.

“…This study determined the data quantity required and identified relevant features to develop Random Forests-based annual groundwater pumping estimates (2008–2020) over the Kansas High Plains aquifer. We predicted pumping at two spatial scales, i.e., point (well) and grid (2 km). …”
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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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