Conditional autoregressive model based on next scale prediction for missing data reconstruction by Shuang Wang, Xiangpeng Wang, Yuhan Yang, Peifan Jiang, Bin Wang, Yuanhao Li

“…Existing transformer-based autoregressive methods flatten two-dimensional seismic data into one-dimensional sequences, disrupting the inherent two-dimensional structure and compromising the spatial locality of seismic information. To address these limitations, we propose a conditional autoregressive model based on next-scale prediction. …”
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Mapping spatial patterns to energetic benefits in groups of flow-coupled swimmers by Sina Heydari, Haotian Hang, Eva Kanso

“…However, the connection between the spatial patterns observed in collectively moving animals and the energetic benefits at each position within the group remains unclear. …”
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Prediction of quality software quality indicators with applied modifications of integrated gradiates methods by Anton Shantyr, Olha Zinchenko, Kamila Storchak, Andrii Bondarchuk, Yuriy Pepa

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The prediction of karst-collapse susceptibility levels based on the ISSA-ELM integrated model by Jiaxin Wang, Ying Yang, Xian Yang, Yulong Lu, Yang Liu, Da Hu, Yongjia Hu

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Regional Tropospheric Delay Prediction Model Based on LSTM-Enhanced Encoder Network by Yuanfang Peng, Chenglin Cai, Zexian Li, Kaihui Lv, Xue Zhang, Yihao Cai

“…In addition, we compared the spatial and temporal properties of the proposed model with those of the GPT3 and ERA5 models. …”
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A Microscopic Prediction Model for Traffic Noise in Adjacent Regions to Arterial Roads by Ming LI, Jizhou LIU

“…Based on the validated method, the spatial distribution of traffic noise near the arterial road is investigated for different traffic scenarios.…”
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Drought Prediction Model of Pearl River Basin Based on SST and Machine Learning by FENG Xin, LIU Yanju, TONG Hongfu, QIAN Shuni

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Research on water quality prediction of Jiangshan Port based on SCV-CBA model by Yiting Xu, Zhaoju Liu

“…CNN was employed to capture spatial patterns in the data, while BiLSTM network modelled temporal dependencies. …”
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19 Predicting daily PM2.5 in Mexico City: A hybrid spatiotemporal modeling approach by Mike He, Ellen Ren, Iván Gutiérrez-Avila, Itai Kloog

“…Objectives/Goals: In recent years, there has been growing interest in the development of air pollution prediction models, particularly in low- and middle-income countries that are disproportionately impacted by the effects of air pollution. …”
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Predicting transient response using data-driven models for ball-impact simulations by Ross Pivovar, Fei Chen, Raghunath Katragadda, Vidyasagar Ananthan

“…This study investigates the application of machine learning (ML) models for predicting transient responses in ball-impact elastodynamics simulations. …”
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Research on Stock Index Prediction Based on the Spatiotemporal Attention BiLSTM Model by Shengdong Mu, Boyu Liu, Jijian Gu, Chaolung Lien, Nedjah Nadia

“…Stock index fluctuations are characterized by high noise and their accurate prediction is extremely challenging. To address this challenge, this study proposes a spatial–temporal–bidirectional long short-term memory (STBL) model, incorporating spatiotemporal attention mechanisms. …”
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Spatio-Temporal Graph Neural Networks for Streamflow Prediction in the Upper Colorado Basin by Akhila Akkala, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi, Pouya Hosseinzadeh, Ayman Nassar

“…This study presents a spatio-temporal graph neural network (STGNN) model for streamflow prediction in the Upper Colorado River Basin (UCRB), integrating graph convolutional networks (GCNs) to model spatial connectivity and long short-term memory (LSTM) networks to capture temporal dynamics. …”
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IGWO-MSVR model for predicting stress in coal seam during drilling process by Jian Tan, Yanfeng Geng, Liangke Xu

“…Furthermore, Back Propagation Neural network model (BP), Spatial Autoregressive model (SAR) and MSVR model were adopted to perform the stress prediction, and the stress prediction accuracy from these models was analyzed. …”
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Automated Cardiac Disease Prediction Using Composite GAN and DeepLab Model by Sohail Jabbar, Umar Raza, Muhammad Asif Habib, Muhammad Farhan, Saqib Saeed

“…Meanwhile, the DeepLab segmentation network exploits inter-slice spatial contexts for precise anatomical quantification. …”
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A Comprehensive Deep Learning System With MGRF Modeling for Predicting Breast Cancer Response to Neoadjuvant Chemotherapy by Ahmed Sharafeldeen, Fatma Taher, Norah Saleh Alghamdi, Eman Alnaghy, Reham Alghandour, Khadiga M. Ali, Sameh Shamaa, Abdelrahman Gamal, Mohammed Ghazal, Sohail Contractor, Ayman El-Baz

“…First, tumor regions are delineated across MRI modalities and then modeled using a translation-invariant Markov-Gibbs random field (MGRF) with analytical parameter estimation to capture modality-specific spatial appearance patterns correlated with NAC response. …”
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On the development of a systemic (biopsychosocial) prediction model for cardiovascular disease. Part I by O. Yu. Shchelkova, M. V. Iakovleva, D. A. Eremina, R. Yu. Shindrikov, N. E. Kruglova, I. A. Gorbunov, E. A. Demchenko

“…The study included 437  patients with coronary heart disease and/or chronic heart failure undergoing surgical treatment.Part  I of the article presents the results of the first 4 stages of the study. 1) A theoretical prediction model based on existing data was developed and empirically tested on different patient populations at various stages of surgical treatment. 2) An overall information database was compiled on the basis of our own research. …”
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Multi-scale machine learning model predicts muscle and functional disease progression by Silvia S. Blemker, Lara Riem, Olivia DuCharme, Megan Pinette, Kathryn Eve Costanzo, Emma Weatherley, Jeff Statland, Stephen J. Tapscott, Leo H. Wang, Dennis W. W. Shaw, Xing Song, Doris Leung, Seth D. Friedman

“…All model stages revealed strong predictive performance in separate holdout datasets. …”
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PM2.5 prediction and its influencing factors in the Beijing-Tianjin-Hebei urban agglomeration using spatial temporal graph convolutional networks by Yawen Zhao

“…To address this, this study uses spatiotemporal analysis and Spatial Temporal Graph Convolutional Networks (ST-GCN) to evaluate the variation and driving factors of PM _2.5 concentrations in the Beijing-Tianjin-Hebei (BTH) urban agglomeration from 2014 to 2024, and to make predictions. …”
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Determination of Spatial-Temporal Correlation Structure of Troposphere Ozone Data in Tehran City by S.S. Mousavi, M. Mohammadzadeh

“…Spatial-temporal modeling of air pollutants, ground-level ozone concentrations in particular, has attracted recent attention because by using spatial-temporal modeling, can analyze, interpolate or predict ozone levels at any location. …”
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Soil Moisture Content Prediction Using Gradient Boosting Regressor (GBR) Model: Soil-Specific Modeling with Five Depths by Tarek Alahmad, Miklós Neményi, Anikó Nyéki

“…Monitoring soil moisture content (SMC) remains challenging due to its spatial and temporal variability. Accurate SMC prediction is essential for optimizing irrigation and enhancing water use efficiency. …”
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