Pattern mining and prediction techniques for user behavioral trajectories in e-commerce. by Xin Wang, Dong-Feng Liu

“…Analyzing user behaviour sequences requires balancing detail with data reduction while addressing challenges such as excessive spatial complexity and potential null results in predictions. …”
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Urban morphology and climate vulnerability assessment in Kuwait: A spatio-temporal predictive analysis utilizing deep neural network-enhanced markov chain models for 2050 and 2100. by Walid Al-Shaar, Xavier Lehmann, Noha Saad, Gremina Elmazi, Mohamad Al-Shaar, Christelle Tohme

“…Utilizing historical LULC data from 1985, 2005, and 2022, along with various spatial drivers, the research predicts urban expansion patterns for 2050 and 2100. …”
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Random fields and up scaling, towards a more predictive probabilistic quantitative hydrogeology by Noetinger, Benoît

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Comparative convolutional neural networks for perovskite solar cell PCE predictions by Milan Harth, D. Kishore Kumar, Said Kassou, Kenza El Idrissi, Ritesh Kant Gupta, Yonatan Daniel, Ofry Makdasi, Iris Visoly-Fisher, Alessio Gagliardi

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Enhancing the Prediction of Dam Deformations: A Novel Data-Driven Approach by Jonas Ziemer, Gideon Stein, Carolin Wicker, Jannik Jänichen, Daniel Klöpper, Katja Last, Joachim Denzler, Christiane Schmullius, Maha Shadaydeh, Clémence Dubois

“…Second, by incorporating freely available PS time series into deformation prediction, dams can be monitored in higher spatial resolution, making PSI a valuable tool for dam operators. …”
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Hybrid machine learning algorithms accurately predict marine ecological communities by Luciana Erika Yaginuma, Luciana Erika Yaginuma, Fabiane Gallucci, Danilo Cândido Vieira, Paula Foltran Gheller, Simone Brito de Jesus, Thais Navajas Corbisier, Gustavo Fonseca

“…This study aims to predict the spatial distribution of nematode associations from 25 m to 2500 m water depth over an area of 350,000 km² and understand the major oceanographic processes influencing them. …”
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A causal intervention framework for synthesizing mobility data and evaluating predictive neural networks by Ye Hong, Yanan Xin, Simon Dirmeier, Fernando Perez-Cruz, Martin Raubal

“…The results demonstrate the effectiveness in producing location sequences with distinct mobility behaviors, thereby facilitating the simulation of diverse yet realistic spatial and temporal changes. These changes result in performance fluctuations in next location prediction networks, revealing impacts of critical mobility behavior factors, including sequential patterns in location transitions, proclivity for exploring new locations, and preferences in location choices at population and individual levels. …”
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Friend Link Prediction Method Based on Heterogeneous Multigraph and Hierarchical Attention by Aoxue Liu, Boyu Li, Yong Wang, Ziteng Yang

“…Key challenges include inadequate modeling of the intricate relationships between users and points of interest (POI), overlooking the significance of spatial-temporal information in user trajectories, and underutilizing rich edge features. …”
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Multibranch Adaptive Fusion Graph Convolutional Network for Traffic Flow Prediction by Xin Zan, Jasmine Siu Lee Lam

“…Urban road networks have complex spatial and temporal correlations, driving a surge of research interest in spatial-temporal traffic flow prediction. …”
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Moisture prediction in chicken litter using hyperspectral data and machine learning by Ahmad Tulsi, Abdul Momin, Victoria Ayres

“…This study addresses that gap by evaluating the feasibility of combining HSI with machine learning models to predict moisture content in chicken litter. …”
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Spatial Weight Matrix Comparison of SAR-X Model using Casetti Approach by Almeira Tsanawafa, Dianne Amor Kusuma, Budi Nurani Ruchjana

“…The Spatial Autoregressive Exogenous (SAR-X) model with the Casetti approach is used to describe the influence of location and exogenous variables in the description and prediction of spatial observations, namely, people's habits and behavior towards culture in Java Island. …”
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Real-time temperature prediction of large-scale lithium battery module driven by data based on few measurement points by Jiajie HAN, Qingyang YUAN, Yu LI, Bo ZHANG, Ke XUE, Tian LAN

“…Accurate real-time temperature prediction in electrochemical energy storage systems plays a critical role in enhancing battery performance, extending lifespan, and preventing thermal runaway, a major safety concern. …”
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Quantitative prediction of water quality in Dongjiang Lake watershed based on LUCC by Yang Song, Xiaoming Li, Ying Zheng, Gui Zhang

“…Current models often lack the ability to effectively predict water quality changes in a dynamic spatio-temporal context, particularly in complex watershed environments. …”
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Temporal and spatial pattern analysis and forecasting of methane: Satellite image processing by Fatima Elshukri, Noor Hussam Abusirriya, Nathan Joseph Braganza, Abdulkarem Amhamed, Odi Fawwaz Alrebei

“…Atmospheric dispersion modeling is a critical tool in environmental research, offering insights into spatial and temporal patterns of pollutants. …”
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Spatial Modeling of Trace Element Concentrations in PM₁₀ Using Generalized Additive Models (GAMs) by Mariacarmela Cusano, Alessandra Gaeta, Raffaele Morelli, Giorgio Cattani, Silvia Canepari, Lorenzo Massimi, Gianluca Leone

“…A stepwise procedure was followed to determine the model with the optimal set of covariates. A leave-one-out cross-validation method was used to estimate the prediction error. …”
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Convolution of the physical point cloud for predicting the self-assembly of colloidal particles by Seunghoon Kang, Young Jin Lee, Kyung Hyun Ahn

“…The phases predicted by our model are not limited to liquid-like dispersions and solid–liquid phase separations, where thermodynamic equilibrium differs, but also include sample-spanning gel structures, where only kinetics differ while thermodynamics remain the same. …”
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Combination of dynamic TOPMODEL and machine learning techniques to improve runoff prediction by Pin‐Chun Huang

“…The present study aims to evaluate the optimal combination of these parameters within the dynamic TOPMODEL framework using machine learning techniques to improve the accuracy of runoff predictions and bolster the model's reliability. An innovative training method is suggested to elevate the model's performance by integrating the Long Short‐Term Memory (LSTM) algorithm and a topological classification, which relies on the evolving spatial distribution of runoff conditions during floods. …”
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Synergistic Evolution and Spatial-Temporal Differences in Green Technological Innovation and Carbon Emission Reduction in the Construction Industry from the Perspective of New Prod... by Zihao Niu, Qingjie Xie

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Explainable Spatio-Temporal Inference Network for Car-Sharing Demand Prediction by Nihad Brahimi, Huaping Zhang, Zahid Razzaq

“…Efficient resource allocation in car-sharing systems relies on precise predictions of demand. Predicting vehicle demand is challenging due to the interconnections of temporal, spatial, and spatio-temporal features. …”
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Gradual Variation-Based Dual-Stream Deep Learning for Spatial Feature Enhancement With Dimensionality Reduction in Early Alzheimer’s Disease Detection by Najmul Hassan, Abu Saleh Musa Miah, Taro Suzuki, Jungpil Shin

“…These features are processed through a dual-stream DL model, where each stream captures complementary spatial features. …”
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