A crop model based on dual attention mechanism for large area adaptive yield prediction by Wei Xiang, Long Long, Zichen Liu, Feng Dai, Yucheng Zhang, Hu Li, Lin Cheng

“…Although existing models have improved accuracy by increasing model complexity and coupling different deep learning models, their generalization performance is poor due to significant spatial differences in crop growth environments, making it difficult to explore common features of crop environments in different regions.To address this issue, this paper comprehensively considers crop growth cycles and environmental factors such as soil and weather, presenting a large-scale crop yield prediction model based on an attention mechanism.The model consists of two modules: time attention module and feature attention module. …”
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Event Prediction Using Spatial–Temporal Data for a Predictive Traffic Accident Approach Through Categorical Logic by Eleftheria Koutsaki, George Vardakis, Nikos Papadakis

“…Finally, we implement the traffic accident prediction model using the Prolog language with the corresponding Queries in JPL.…”
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Deep learning-based time series prediction for precision field crop protection by Tao He, Meijin Li, Dong Jin

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OceanLSTM: xLSTM with spatial attention for salt spray formation and migration prediction in marine hot-humid environments by Chuan Chen, Ganxin Jie, Jun Wang, Shouhe Wang, Xiaonong Xu

Subjects: “…salt spray prediction…”
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Spatial and Temporal Changes and Prediction of Habitat Quality in Key Ecological Function Area of Hu'nan Province by Zheng Yunyou, Liu Yanting, Yao Peng, Xie Xianjun, Zhang Guangjie, Deng Chuxiong

“…[Methods] The land use transfer matrix was obtained based on the land use change data of 2009, 2012, 2015, 2018 and 2021, and the spatial-temporal distribution characteristics of land use structure and habitat quality in Nanyue key ecological function area were analyzed and predicted by InVEST model and CA-Markov model. …”
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Spatial and temporal evolution and prediction of soil erosion in the urban agglomeration on the northern slopes of the Tianshan Mountains in China by Zhaojin Yan, Fulin Mao, Rong He, Hui Yang, Hui Ci, Ran Wang

“…To better understand the changes in soil erosion and future development trends of the urban agglomeration on the northern slopes of the Tianshan Mountains, multi-source data on soil, topography, and meteorology were utilized with the RUSLE model to evaluate spatial and temporal characteristics, and the CA-Markov model was used to predict land use/land cover (LULC) changes and soil erosion conditions under various scenarios. …”
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Machine Learning Traffic Flow Prediction Models for Smart and Sustainable Traffic Management by Rusul Abduljabbar, Hussein Dia, Sohani Liyanage

“…Specifically, this paper evaluates the impact of multisource sensor inputs and spatial detector interactions on machine learning-based traffic flow prediction. …”
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Prediction of land use around urban metro stations using the CA-Markov model by Xingzhong Nong, Ming Geng, Xia Jia

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SPATIAL MODEL OF BUILT-IN LAND CHANGE (NDBI) IN LANGSA CITY USING CELLULAR AUTOMATA MARKOV (CA-MARKOV) by Kania Maulia Rizky, Triyatno Triyatno

“…The method used in this study is a spatial-based quantitative method with the Cellular Automata Markov approach to create built-up land modelling in Langsa City, and the Analytical Hierarchy Process method to identify variables that influence changes in built-up land in Langsa City. …”
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Pedestrian trajectory prediction model based on self-supervised spatiotemporal graph network by Shiji Yang, Xuezhong Xiao

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A homotopy estimation based temporal-spatial spectrum prediction for UAV communications with arbitrary flight paths by Shan Luo, Wenjun Zhou, Lifan Wu, Qixiang Zhang, Rongping Lin, Yao Yan, Hui Li, Siyu Xie

“…This paper introduces a temporal-spatial spectrum prediction approach for arbitrary flight within a specific region. …”
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The model for grain wheat yield prediction at high spatial resolution based on physical-geographical properties and satellite vegetation indices by Dragana Blagojević, Nina Pajević, Gordan Mimić, Stefanija Ćuković, Slobodan B. Marković, Bernardo Maestrini, Sanja Brdar

“…The Random Forest regression model on data from diverse sources at the 10-meter spatial resolution was developed. …”
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Topography-Enhanced Multilevel Residual Attention U-Net Model for Sea Ice Concentration Spatial Super-Resolution Prediction by Jianxin He, Yuxin Zhao, Shuo Yang, Haoyu Wang, Xiong Deng

“…To address these challenges, this article proposes a TE-MRAU-Net downscaling model. TE-MRAU-Net integrates three innovative modules: the HR topography feature module, which introduces static topographic constraints to effectively improve reconstruction accuracy along sea–land boundaries; the multilevel residual module, which enhances the model’s ability to extract fine-scale sea ice features in super-resolution predictions; and the spatial attention connector module, which strengthens spatial modeling and structural consistency, particularly improving reconstruction performance in marginal sea ice edges and lower latitude Arctic regions. …”
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Spatial Prediction of Soil Water Content by Bayesian Optimization–Deep Forest Model with Landscape Index and Soil Texture Data by Weihao Yang, Ruofan Zhen, Fanyue Meng, Xiaohang Yang, Miao Lu, Yingqiang Song

“…A Bayesian optimization–deep forest (BO–DF) model was developed to leverage these indices for predicting the spatial variability of SWC. …”
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Separable Reversible Data Hiding in Encrypted 3D Mesh Models Based on Spatial Clustering and Multi-MSB Prediction by Y. Mao, Y. Xu

“…To address this, a method combining spatial clustering with multi-MSB (multiple most significant bit) prediction is proposed to enhance embedding rate and capacity. …”
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Coupled intelligent prediction model for medium- to long-term runoff based on teleconnection factors selection and spatial-temporal analysis. by Jintao Li, Ping Ai, Chuansheng Xiong, Yanhong Song

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Spatial analysis and prediction of psittacosis in Zhejiang Province, China, 2019–2024 by Zheyuan Ding, Haocheng Wu, Chen Wu, Kui Liu, Qinbao Lu, Xinyi Wang, Tianying Fu, Junjie Li, Ke Yang, Queping Song, Junfen Lin

“…Demographic characteristics and seasonal trends were systematically analyzed. Spatial epidemiological methods, including spatiotemporal distribution mapping, spatial autocorrelation analysis, and Kriging interpolation, were employed to identify disease hotspots and predict risk areas.ResultsDuring the study period, 315 psittacosis cases were reported, with an annual average incidence rate of 0.0914 per 100,000 population, showing a significant increasing trend. …”
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STGAT: Spatial–Temporal Graph Attention Neural Network for Stock Prediction by Ruizhe Feng, Shanshan Jiang, Xingyu Liang, Min Xia

“…Additionally, deep learning methods, especially temporal convolution networks and graph attention networks, have been introduced in this area and have achieved significant improvements in both stock price prediction and portfolio optimization. Therefore, this study proposes a Spatial–Temporal Graph Attention Network (STGAT) that integrates STL decomposition components and graph structures to model both temporal patterns and asset correlations. …”
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New multifactor spatial prediction method based on Bayesian maximum entropy by YANG Yong, ZHANG Chutian, HE Liyuan

“…Currently, the spatial distribution of soil properties is usually predicted with classical geostatistics or environmental correlation. …”
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Multi-Task Learning-Based Traffic Flow Prediction Through Highway Toll Stations During Holidays by Xiaowei Liu, Yunfan Zhang, Zhongyi Han, Hao Qiu, Shuxin Zhang, Jinlei Zhang

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