Autonomous Driving Decision-Making Method Based on Spatial-Temporal Fusion Trajectory Prediction by Yutao Luo, Aining Sun, Jiawei Hong

“…In this paper, we propose a driving strategy learning method based on spatial-temporal feature prediction. Firstly, the spatial interaction between vehicles is implicitly modeled using a graph convolutional neural network and multi-head attention mechanism, and the gated loop unit is embedded to capture the sequential temporal relationship to establish a prediction model incorporating spatial-temporal features. …”
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Self-adaptive spatial-temporal network based on heterogeneous data for air quality prediction by Feng Chang, Liang Ge, Siyu Li, Kunyan Wu, Yaqian Wang

“…However, accurately predicting future air quality remains a challenging task because of the complex spatial-temporal dependencies of air quality. …”
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Dynamic Spatial–Temporal Graph Neural Network for Cooling Capacity Prediction in HVDC Systems by Hao Sun, Shaosen Li, Jianxiang Huang, Hao Li, Guanxin Jing, Ye Tao, Xincui Tian

“…Traditional machine learning methods, while effective in static scenarios, struggle to capture these dependencies, and existing deep learning models often lack the ability to jointly model spatial and temporal relationships. …”
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Exploration of geo-spatial data and machine learning algorithms for robust wildfire occurrence prediction by Svetlana Illarionova, Dmitrii Shadrin, Fedor Gubanov, Mikhail Shutov, Usman Tasuev, Ksenia Evteeva, Maksim Mironenko, Evgeny Burnaev

“…The goal of this study is to explore the potential of predicting wildfire occurrences using various available environmental parameters - meteorological, geo-spatial, and anthropogenic - and machine learning (ML) algorithms. …”
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Predicting spatial spread of rabies in skunk populations using surveillance data reported by the public. by Kim M Pepin, Amy J Davis, Daniel G Streicker, Justin W Fischer, Kurt C VerCauteren, Amy T Gilbert

“…We developed a dynamic patch-occupancy model which predicts spatio-temporal spreading while accounting for heterogeneous sampling. …”
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Transformer based spatially resolved prediction of mechanical properties in wire arc additive manufacturing by Mohammad Keshmiri, Shirin Dehgahi, Abdullah Mohiuddin, Ahmed J. Qureshi

“…The results demonstrate that the framework achieves good prediction capabilities using a small dataset. It provides a state-of-the-art methodology for predicting the spatial and temporal evolution of mechanical properties leveraging the transformer architecture. …”
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A Hybrid Long Short-Term Memory-Graph Convolutional Network Model for Enhanced Stock Return Prediction: Integrating Temporal and Spatial Dependencies by Songze Shi, Fan Li, Wei Li

“…This study proposes a hybrid model integrating long short-term memory (LSTM) networks and graph convolutional networks (GCNs) to enhance accuracy by capturing both temporal dynamics and spatial inter-stock relationships. …”
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Predicting the effect of landscape structure on epidemic invasion using an analytical estimate for infection rate by Yevhen F. Suprunenko, Stephen J. Cornell, Christopher A. Gilligan

“…We explore the potential of using an analytical approximation for the rate, [Formula: see text], at which susceptible crop fields become infected at the start of an epidemic to predict the effect that the spatial structure of a host landscape will have on an epidemic. …”
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Temporal-Spatial Redundancy Reduction in Video Sequences: A Motion-Based Entropy-Driven Attention Approach by Ye Yuan, Baolei Wu, Zifan Mo, Weiye Liu, Ji Hong, Zongdao Li, Jian Liu, Na Liu

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Vers une nouvelle classification des modèles d'évaluation et de prédiction de l'érosion hydrique by Aïcha Fadil, Farid El Wahidi

“…However, these models contrast considerably regarding to the studied phenomena, their nature and their complexity in the field, and to the implemented approach, through the variety and the quality of used data, the spatial and temporal scales of application and the information obtained as output, types and uncertainties.An attempt to classify water erosion assessment models (129 models are considered) is presented based on their most critical geospatial attributes for water erosion modeling. …”
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Exploring suicidal thoughts among prospective university students: a study with applications of machine learning and GIS techniques by Mohammed A. Mamun, Firoj Al-Mamun, Md Emran Hasan, Nitai Roy, Moneerah Mohammad ALmerab, David Gozal, Md. Shakhaoat Hossain

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Machine learning-driven framework for realtime air quality assessment and predictive environmental health risk mapping by M. Rajesh, R. Ganesh Babu, Usha Moorthy, Sathishkumar Veerappampalayam Easwaramoorthy

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Extended Generalized Skew Laplace Random Field: Spatial Autoregressive and Moving Average Model for Prediction of Missing Data in Skew and Heavy Tailed Data by Mohammad Mehdi Saber, Alireza Nematollahi, Mohsen Mohammadzadeh

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Genetic Programming-Based Prediction Model for Microseismic Data by Man Wang, Hongwei Zhou, Dongming Zhang, Yingwei Wang, Weihang Du, Beichen Yu

“…Therefore, we collected a large amount of microseismic data obtained from the working faces of Shoushan Mine of Pingdingshan Coal Group in China and filtered the data, then we constructed a prediction model of microseismic data based on underground spatial three-dimensional coordinates using genetic programming (GP), which can realize real-time monitoring and disaster warning of microseismic signals. …”
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Research on rock burst prediction based on an integrated model by Junming Zhang, Qiyuan Xia, Hai Wu, Sailei Wei, Zhen Hu, Bing Du, Yuejing Yang, Huaixing Xiong

“…Additionally, the sparrow search algorithm (SSA) is employed to optimize hyperparameters, further improving the model’s performance. Unlike traditional approaches that rely on time-axis-based analysis, this study uses the working face advancement distance as the basis for prediction, which better reveals the potential spatial correlations of rockburst occurrences, aligning with engineering practice needs.Validation using microseismic monitoring data from a coal mine demonstrates that the proposed model achieves a prediction accuracy of 93.62% and an F1-score of 93.54%. …”
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Prediction of the change in suitable growth area of Sabina tibetica on the Qinghai-Tibetan plateau using MaxEnt model by Xiaoxiong Li, Dongsheng Yang, Jingjie Wang, Gang Pan

“…We employed the MaxEnt model with 10 bioclimatic and topographic variables to predict its distribution shifts under RCP4.5 and RCP8.5 scenarios for 2050 and 2070. …”
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A Convolutional Neural Network-Weighted Cellular Automaton Model for the Fast Prediction of Urban Pluvial Flooding Processes by Jiarui Yang, Kai Liu, Ming Wang, Gang Zhao, Wei Wu, Qingrui Yue

“…Abstract Deep learning models demonstrate impressive performance in rapidly predicting urban floods, but there are still limitations in enhancing physical connectivity and interpretability. …”
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A spatiotemporal model for urban taxi Origin–Destination prediction based on Multi-hop GCN and Hierarchical LSTM by Jiang Rong, Wangtu Xu, Yanjie Wen

“…On the PEMS08, the single-path version of DBSTNet outperforms state-of-the-art demand prediction models with an 8.29% increase in SMAPE, underscoring the scalability of the proposed MS-HT for spatial–temporal computation. …”
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Modelling the Current and Future Pollutant Emission from Non-Road Machinery: A Case Study in Shanghai by Rui Chen, Xuerui Yang, Lei Xi, Huajun Zhen, Guangli Xiu

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MDA-MIM: a radar echo map prediction model integrating multi-scale feature fusion and dual attention mechanism by HU Qiang, GAO Yating, YIN Binli, QU Lianen

“…Experiments conducted on real-world datasets demonstrate that MDA-MIM achieves state-of-the-art predictive performance, consistently outperforming baseline models across multiple evaluation metrics, including MSE, MAE, SSIM, and PSNR.…”
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