Few-shot hotel industry site selection prediction method based on meta learning algorithms and transportation accessibility by Na Li, Huaishi Wu

“…First, the initial location prediction results are obtained through the meta-model. …”
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Prediction for Various Drought Classes Using Spatiotemporal Categorical Sequences by Rizwan Niaz, Mohammed M. A. Almazah, Xiang Zhang, Ijaz Hussain, Muhammad Faisal

“…Drought frequently spreads across large spatial and time scales and is more complicated than other natural disasters that can damage economic and other natural resources worldwide. …”
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A flexible framework for local-level estimation of the effective reproductive number in geographic regions with sparse data by Md Sakhawat Hossain, Ravi Goyal, Natasha K. Martin, Victor DeGruttola, Mohammad Mihrab Chowdhury, Christopher McMahan, Lior Rennert

“…Methods To overcome this challenge, we propose a two-step approach that incorporates existing $$\:{R}_{t}$$ estimation procedures (EpiEstim, EpiFilter, EpiNow2) using data from geographic regions with sufficient data (step 1), into a covariate-adjusted Bayesian Integrated Nested Laplace Approximation (INLA) spatial model to predict $$\:{R}_{t}$$ in regions with sparse or missing data (step 2). …”
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Brain systems for probabilistic and dynamic prediction: computational specificity and integration. by Jill X O'Reilly, Saad Jbabdi, Matthew F S Rushworth, Timothy E J Behrens

“…We contrasted the neural systems associated with two computationally distinct forms of predictive model: a reinforcement-learning model of the environment obtained through experience with discrete events, and continuous dynamic forward modeling. …”
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Satellite-Based Prediction of Water Turbidity Using Surface Reflectance and Field Spectral Data in a Dynamic Tropical Lake by Elsa Pereyra-Laguna, Valeria Ojeda-Castillo, Enrique J. Herrera-López, Jorge del Real-Olvera, Leonel Hernández-Mena, Ramiro Vallejo-Rodríguez, Jesús Díaz

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An adaptive spatiotemporal dynamic graph convolutional network for traffic prediction by Zhiguo Xiao, Qi Shen, Changgen Li, Dongni Li, Qian Liu

“…To address these limitations, we propose an adaptive spatiotemporal dynamic graph convolutional network (AST-DGCN) for traffic prediction. Under the encoder-decoder architecture, the proposed model leverages node embedding techniques to extract high-dimensional features, generating time-evolving adaptive graphs through self-attention mechanisms. …”
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A Study of Tool Wear Prediction Based on Digital Twins by LIU Minghao, MAO Xinhui, XIA Wei, YUE Caixu, LIU Xianli

“…This model can deeply extract spatial features and dynamic temporal features, significantly improving prediction accuracy compared to conventional models. …”
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Bayesian geostatistical analysis and prediction of Rhodesian human African trypanosomiasis. by Nicola A Wardrop, Peter M Atkinson, Peter W Gething, Eric M Fèvre, Kim Picozzi, Abbas S L Kakembo, Susan C Welburn

“…Here we extend this study to account for spatial autocorrelation, incorporate uncertainty in input data and model parameters and undertake predictive mapping for risk of high HAT prevalence in future.…”
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Modified STARIMA model for space-time data by Laura Šaltytė

“… In this paper we propose spatial time series model. ARIMA model class is considered for each location. …”
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RUL Prediction of DC Contactor Using CNN-LSTM With Channel Attention and Fusion of Dual Aggregated Features by Sai Wang, Yuanfeng Zhang, Hao Huang, Yun Shi, Jianfei Si

“…Key features were extracted, preprocessed, and used to train and evaluate the model. Results show that the DAF-CA-CNN-LSTM model significantly outperforms traditional LSTM and CNN-LSTM models in RUL prediction, achieving higher accuracy and robustness in complex, noisy environments. …”
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The Prediction of Multistep Traffic Flow Based on AST-GCN-LSTM by Fan Hou, Yue Zhang, Xinli Fu, Lele Jiao, Wen Zheng

“…Aiming at the traffic flow prediction problem of the traffic network, this paper proposes a multistep traffic flow prediction model based on attention-based spatial-temporal-graph neural network-long short-term memory neural network (AST-GCN-LSTM). …”
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Modeling future projections of land use and land cover in the Kakataibo territory by J. E. Victorio Gonzales, K. Hopfgartner

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Spatio-Temporal Data Augmentation Method for Network Traffic Prediction by Sung Oh, Myeong-Jun Oh, Jong-Kyung Im, Ji-Yeon Park, Joung-Sik Kim, Na-Rae Yi, Myung-Ho Kim, Sung-Ho Bae

“…Despite this need, existing studies have largely overlooked data augmentation techniques that simultaneously address spatial and temporal features. Moreover, network traffic data often exhibits localized and granular patterns, meaning that augmented data with significant spatial deviations from the original distribution can undermine structural consistency, leading to severe performance degradation in prediction models. …”
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Forecasting Lattice and Point Spatial Data: Comparison of Unilateral and Multilateral SAR Models by Carlo Grillenzoni

“…Spatial auto-regressive (SAR) models are widely used in geosciences for data analysis; their main feature is the presence of weight (W) matrices, which define the neighboring relationships between the spatial units. …”
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The Impact of Radiosounding Observations on Numerical Weather Prediction Analyses in the Arctic by T. Naakka, T. Nygård, M. Tjernström, T. Vihma, R. Pirazzini, I. M. Brooks

“…Abstract The radiosounding network in the Arctic, despite being sparse, is a crucial part of the atmospheric observing system for weather prediction and reanalysis. The spatial coverage of the network was evaluated using a numerical weather prediction model, comparing radiosonde observations from Arctic land stations and expeditions in the central Arctic Ocean with operational analyses and background fields (12‐hr forecasts) from European Centre for Medium‐Range Weather Forecasts for January 2016 to September 2018. …”
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Integration of geospatial techniques and machine learning in land parcel prediction by Nekkanti Haripavan, Subhashish Dey, Chimakurthi Harika Mani Chandana

“…Researchers and practitioners can customize their models by choosing the most pertinent variables for each land parcel forecasts from a wide range of spatial features. …”
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Examination of analytical shear stress predictions for coastal dune evolution by O. Cecil, N. Cohn, M. Farthing, S. Dutta, S. Dutta, A. Trautz

“…<p>Existing process-based models for simulating coastal foredune evolution largely use the same analytical approach for estimating wind-induced surface shear stress distributions over spatially variable topography. …”
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Impact of Thermospheric Mass Density on the Orbit Prediction of LEO Satellites by Changyong He, Yang Yang, Brett Carter, Kefei Zhang, Andong Hu, Wang Li, Florent Deleflie, Robert Norman, Suqin Wu

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A Novel Empirical Interpolation Surrogate for Digital Twin Wave-Based Structural Health Monitoring with MATLAB Implementation by Abhilash Sreekumar, Linjun Zhong, Dimitrios Chronopoulos

“…By greedily selecting key spatial, temporal, and parametric points, our approach builds an affine-like reduced model without modifying the underlying operators. …”
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Bayesian Adaptive Lasso for the Partial Functional Linear Spatial Autoregressive Model by Dengke Xu, Ruiqin Tian, Ying Lu

“…This study introduces a partial functional linear spatial autoregressive model which can explore the relationship between a scalar spatially dependent response variable and predictive variables containing both multiple scalar covariates and a functional covariate. …”
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