Dynamic Graph-Based Clustering for Non-Stationary Spatio-Temporal Event Prediction by Vasavi M., Murugan A., Sharma K. Venkatesh

“…Representation of Graph gives us the crime data analysis with location wise and helps us to predict the next occurrence instance. An alternate way of modeling the objects in data sets is to represent those using graphs. …”
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3D long time spatiotemporal convolution for complex transfer sequence prediction by Qiu Yunan, Cui Yingjie, Tang Haibo, Chen Zhongfeng, Lu Zhenyu, Xue Feng

“…However, two challenges still exist in the existing methods: 1) Most of the existing spatio-temporal prediction tasks focus on extracting temporal information using recurrent neural networks and using convolution networks to extract spatial information, but ignore the fact that the forgetting of historical information still exists as the input sequence length increases. 2) Spatio-temporal sequence data have complex non-smoothness in both temporal and spatial, such transient changes are difficult to be captured by existing models, while such changes are often particularly important for the detail reconstruction in the image prediction task. …”
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GMTP: Enhanced Travel Time Prediction with Graph Attention Network and BERT Integration by Ting Liu, Yuan Liu

“…(1) Background: Existing Vehicle travel time prediction applications face challenges in modeling complex road network and handling irregular spatiotemporal traffic state propagation. (2) Methods: To address these issues, we propose a Graph Attention-based Multi-Spatiotemporal Features for Travel Time Prediction (GMTP) model, which integrates an enhanced graph attention network (GATv2) and Bidirectional Encoder Representations from Transformers (BERT) to analyze dynamic correlations across spatial and temporal dimensions. …”
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Enhancing bathymetric prediction by integrating gravity and gravity gradient data with deep learning by Junhui Li, Nengfang Chao, Nengfang Chao, Houpu Li, Gang Chen, Gang Chen, Shaofeng Bian, Zhengtao Wang, Aoyu Ma

“…This study aims to enhance the spatial resolution and accuracy of bathymetric prediction by integrating Gravity Anomaly (GA) and Vertical Gravity Gradient Anomaly (VGG) data with a dual-channel Backpropagation Neural Network (BPNN). …”
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Overview of Applications and Research Directions of Deep Learning Methods for Wind Power Prediction by LIU Tan, LIU Na, LIU Guiping, LIU Kunjie, LIU Min, ZHUANG Xufei, ZHANG Zhonghao

“…The application of deep learning technology in wind power prediction is reviewed, and on the basis of making a careful division of deep learning technology, it focuses on analyzing the overcome problems and performance by spatial structure-based deep learning models and time-based deep learning models and their related variants, and summarizes the limitations of the proposed modeling methods and the corresponding solutions. …”
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Spatio-temporal transformer and graph convolutional networks based traffic flow prediction by Jin Zhang, Yimin Yang, Xiaoheng Wu, Sen Li

“…To address these issues, a novel deep learning-based traffic flow prediction model, TDMGCN, is proposed. It integrates the Transformer and a multi-graph GCN to tackle the limitations of long-term prediction and the challenges of using the predefined adjacency matrices for spatial correlation extraction. …”
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A method for spatial interpretation of weakly supervised deep learning models in computational pathology by Abhinav Sharma, Bojing Liu, Mattias Rantalainen

“…Such information is also needed for any further spatial interpretation of predictions from such models. …”
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Historical data analysis and future prediction of lung cancer in Zhejiang province, China by Yang Gao, Mengqi Zhang, Guangyuan Liu, Huiyue Lin, Hong Huang, Hongli Xie

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Prediction and Risk Assessment of Extreme Weather Events Based on Gumbel Copula Function by Peng-Hui Yang, Yao Yu, Feng Gu, Meng-Jie Qu, Jia-Ming Zhu

“…Finally, a wavelet neural network model is constructed to predict the probability of extreme weather events throughout the Americas.…”
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GAN-based image prediction of maize growth across varieties and developmental stages by Xinyi Wang, Shilong Liu, Zhihao Wang, Zedong Geng, Weikun Li, Chengxiu Wu, Yingjie Xiao, Wanneng Yang, Lingfeng Duan

“…Results This article proposed a visualized growth prediction method based on an improved Pix2PixHD network, incorporating spatial attention mechanisms, an improved loss function, and a modified dropout strategy to enhance prediction accuracy and visual fidelity. …”
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Deep spatio-temporal dependent convolutional LSTM network for traffic flow prediction by Jie Tang, Rong Zhu, Fengyun Wu, Xuansen He, Jing Huang, Xianlai Zhou, Yishuai Sun

“…Secondly, for time features, most scholars use time series prediction models, such as recurrent neural networks and their variants. …”
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Checkpoint data-driven GCN-GRU vehicle trajectory and traffic flow prediction by Deyong Guan, Na Ren, Ke Wang, Qi Wang, Hualong Zhang

“…The results show that compared with the single models GCN, GRU, BiGRU, and BiLSTM, the GCN-GRU model has reduced the MAE by 0.75, 0.46, 0.52, and 0.57, and the RMSE by 0.76, 0.52, 0.58, and 0.68, respectively, demonstrating stronger spatial and temporal correlation characteristics and higher prediction accuracy. …”
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Surrogate model for calculating spatial magnetic track forces in permanent magnet levitation transportation by LIU Haodong, FENG Yang, ZHAO Chunfa, LUO Shihui, SONG Xiaolin

“…Developing a method for rapidly and accurately calculating magnetic forces, which considers multi-degree-of-freedom spatial attitudes, is essential for the dynamic prediction and assessment of PML vehicles. …”
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Evaluation of machine learning and deep learning algorithms for fire prediction in Southeast Asia by Aditya Eaturu, Krishna Prasad Vadrevu

“…By tailoring models to specific regional fire data, prediction accuracy and responsiveness can be enhanced, ultimately improving fire risk management in Southeast Asia and beyond.…”
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The spatiotemporal evolution and prediction of vegetation NPP in the Huangshui River Basin of Qilian Mountains by Sujing Ding, Qiang Sun, Yan Guo, Xiao Wei

“…Subsequently, the ConvGRU spatiotemporal prediction model was employed to investigate the prospective trajectory of vegetation NPP in the HRB. …”
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Digital mapping of Ghana’s soil properties and nutrients: performance of spline and weighted average approaches by Kora B.D. Simperegui, Anselme K.K. Kouame, Benjamin Kwesie, Prem S. Bindraban, William Adzawla, Eric Asamoah, Mohamed El Gharous

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Spatial age-stratified epidemiological model with applications to South African COVID-19 pandemic by Raeesa Manjoo-Docrat, Nada Abdelatif, Jenny Holloway, Nontembeko Dudeni-Tlhone, Claudia Dresselhaus, Elona Mbayise, Charl Janse van Rensburg, Inger Fabris-Rotelli, Pravesh Debba, Sibusisiwe Makhanya

“…Results: The spatial age-stratified model produced more biologically plausible and accurate predictions compared to non-stratified models investigated. …”
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Temporal and Spatial Variation in Habitat Quality in Guangxi Based on PLUS-InVEST Model by Chuntian Pan, Jun Wen, Jianing Ma

“…This study systematically analyzes the spatial and temporal dynamics of land use and habitat quality in Guangxi from 2000 to 2020 using the PLUS-InVEST model and simulates future scenarios for 2030. …”
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LSTGINet: Local Attention Spatio-Temporal Graph Inference Network for Age Prediction by Yi Lei, Xin Wen, Yanrong Hao, Ruochen Cao, Chengxin Gao, Peng Wang, Yuanyuan Guo, Rui Cao

“…First, multi-scale temporal and spatial branches are used to increase the receptive field and model the age information simultaneously, achieving the perception of static correlation. …”
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Multimodal Learning for Traffic Risk Prediction: Combining Aerial Imagery With Contextual Data by Hanlin Tian, Yuxiang Feng, Mohammed Quddus, Yiannis Demiris, Panagiotis Angeloudis

“…Using DeepLabV3+, UNet++, and SegFormer as baseline models, we demonstrate that combining building and traffic data enhances prediction accuracy compared to models relying solely on visual data. …”
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