Traffic accident risk prediction based on deep learning and spatiotemporal features of vehicle trajectories. by Hao Li, Linbing Chen

“…The model extracts spatial features such as vehicle speed, acceleration, and lane-changing distance through CNN, captures temporal dependencies in trajectories using LSTM, and effectively models the complex spatial structure of traffic networks with GNN, thereby improving prediction accuracy.The main contributions of this paper are as follows: First, an innovative combined model is proposed, which comprehensively considers spatiotemporal features and road network relationships, significantly improving prediction accuracy. …”
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PGDRT: Prediction Demand Based on Graph Convolutional Network for Regional Demand-Responsive Transport by Eunkyeong Lee, Hosik Choi, Do-Gyeong Kim

“…In this study, a graph convolutional network model that performs demand prediction using spatial and temporal information was developed. …”
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An Intelligent Trajectory Prediction Algorithm for Hypersonic Glide Targets Based on Maneuver Mode Identification by Mingjie Li, Chijun Zhou, Lei Shao, Humin Lei

“…The proposed prediction method is designed to improve the prediction accuracy by combining the target dynamic model with the flight data. …”
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A Multihierarchy Flow Field Prediction Network for Multimodal Remote Sensing Image Registration by Wenqing Wang, Kunpeng Mu, Han Liu

“…Therefore, this article proposes a multimodal remote sensing image registration method that uses multihierarchy flow field cumulative prediction at different scales. The method consists of a multiscale feature pyramid, a dense feature matching module, a swin-transformer flow field prediction, and a spatial transformation module. …”
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Improving Doppler Radar Precipitation Prediction with Citizen Science Rain Gauges and Deep Learning by Marshall Rosenhoover, John Rushing, John Beck, Kelsey White, Sara Graves

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Bridge Deformation Prediction Using KCC-LSTM With InSAR Time Series Data by Zechao Bai, Chang Shen, Yanping Wang, Yun Lin, Yang Li, Wenjie Shen

“…Given that bridge is complex, singular structures with unique spatial-temporal characteristics, existing methods designed for land subsidence are not directly applicable to bridge deformation prediction. …”
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Integrated CNN‐LSTM for Photovoltaic Power Prediction based on Spatio‐Temporal Feature Fusion by Junwei Ma, Meiru Huo, Jinfeng Han, Yunfeng Liu, Shunfa Lu, Xiaokun Yu

“…Due to the variability of different neural networks, the prediction results of the integrated model are often higher than the best‐performing individual model. …”
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Lightweight pose estimation spatial-temporal enhanced graph convolutional model for miner behavior recognition by WANG Jianfang, DUAN Siyuan, PAN Hongguang, JING Ningbo

“…MEST-GCN improved upon the spatial-temporal graph convolutional network (ST-GCN) by removing redundant layers to simplify the model structure and reduce the number of parameters. …”
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FloodGNN-GRU: a spatio-temporal graph neural network for flood prediction by Arnold Kazadi, James Doss-Gollin, Antonia Sebastian, Arlei Silva

“…In this paper, we propose FloodGNN-GRU, a spatio-temporal flood prediction model that combines a graph neural network (GNN) and a gated recurrent unit (GRU) architecture. …”
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Global surface eddy mixing ellipses: spatio-temporal variability and machine learning prediction by Tian Jing, Ru Chen, Chuanyu Liu, Chunhua Qiu, Chunhua Qiu, Cuicui Zhang, Mei Hong

“…All three models effectively represent and predict spatiotemporal variations, with the STN model, which incorporates an adaptive spatial attention mechanism, outperforming RF and CNN models in predicting mixing anisotropy. …”
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Efficient Inter-Prediction Method Using Reference Frame Accumulation for MPEG G-PCC by Xin Li, Eun-Young Chang, Jihun Cha, Euee S. Jang

“…However, this single-reference approach may not fully capture the temporal and spatial correlations between frames, potentially limiting prediction performance. …”
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Prediction of Sea Surface Current Around the Korean Peninsula Using Artificial Neural Networks by Jeong‐Yeob Chae, Hyunkeun Jin, Inseong Chang, Young Ho Kim, Young‐Gyu Park, Young Taeg Kim, Boonsoon Kang, Min‐su Kim, Ho‐Jeong Ju, Jae‐Hun Park

“…Continuous improvements in numerical models have made it possible to predict more realistic oceans using data assimilation and fine spatial resolution. …”
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A machine learning approach to predict phyllosphere resistome abundance across urbanization gradients by Rui-Ao Ma, Yi-Hui Ding, Shifa Zhong, Ting-Ting Jing, Xuechu Chen, Si-Yu Zhang

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STNet: Prediction of Underwater Sound Speed Profiles with an Advanced Semi-Transformer Neural Network by Wei Huang, Junpeng Lu, Jiajun Lu, Yanan Wu, Hao Zhang, Tianhe Xu

“…Comparative experimental results revealed that STNet outperformed state-of-the-art models in predictive accuracy and maintained good computational efficiency, demonstrating its potential for enabling accurate long-term full-depth ocean SSP forecasting.…”
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Identification of Dominant Factors and Scenario Prediction of Ecosystem Services in the Jialing River Basin, China by Xingyue Guo, Tian Wang, Zhanbin Li, Jiao Zhang, Kunxia Yu, Peng Li, Yingying Geng, Heng Wu, Ganggang Ke

“…Taking the Jialing River Basin as the research object, this paper elucidates the spatial and temporal evolution characteristics of various ecosystem services, identifies the main factors affecting these functions, and predicts the spatial distribution characteristics of the basin’s ecosystem service functions under future scenario models. …”
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Quality Measurement and Optimization Prediction of Urban Waterfront Fitness Space Based on Behavior Simulation by Chunxia YANG, Ruoyao MIN

“…Based on the multi-agent behavior simulation technology and waterfront fitness quality assessment system, this research conducts a quality measurement of nine typical waterfront public spaces along the Huangpu River, and explores the possibility of further optimizing the spatial quality of the Minsheng Wharf area, aiming to provide a systematic method for quality assessment and optimization prediction. …”
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Landslide dynamic hazard prediction based on precipitation variation trend and backpropagation neural network by Ruixuan Huang, Bin Zeng, Dong Ai, Jingjing Yuan, Huiyuan Xu

“…Taking the Qingjiang Reservoir landslide in Changyang County, Hubei Province, China as an example, based on dynamic precipitation data and the BPNN model were used to develop a dynamic landslide hazard prediction model, and the temporal assessment and spatial distribution results of slope unit hazards in the study area from the 1980s to the 2010s, 2025, and 2030 were evaluated and predicted. …”
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UAV-based multispectral image analytics and machine learning for predicting crop nitrogen in rice by Suyog Balasaheb Khose, Damodhara Rao Mailapalli

“…The study aims to develop a robust machine learning-based model for predicting rice crop SPAD values using spectral data and to generate spatial maps of SPAD values and nitrogen content. …”
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An Improved Spatio-Temporal Network Traffic Flow Prediction Method Based on Impedance Matrix by Wenhao Li, Yanyan Chen, Yuyan Pan, Yunchao Zhang

“…Effective traffic management and congestion reduction heavily rely on accurate traffic flow prediction. Existing prediction methods, such as Markov, ARIMA, STANN, GLSTM, and DCRNN models, often face challenges because they rely on fixed spatial relationships, leading to limited long-term prediction accuracy. …”
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The missing link: Predicting connectomes from noisy and partially observed tract tracing data. by Max Hinne, Annet Meijers, Rembrandt Bakker, Paul H E Tiesinga, Morten Mørup, Marcel A J van Gerven

“…We apply the methodology to two connectivity data sets of the macaque, where we demonstrate that the latent space model is successful in predicting unobserved connectivity, outperforming two baselines and an alternative model in nearly all cases. …”
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