Investigating the Impact of Climate Change on the Effective Indicators in Desertification and Predicting its Spatial Changes by Azam Sadat Hosseini Khezr Abad, Abassali Vali, Amirhossein Halabian, Mohammad Hossein Mokhtari, Seyyed Ali Mousavi

“…Excessive dryness of arid regions has increased the intensity and spread of desertification. Investigating spatial and temporal patterns of desertification caused by climate change in the northwest of Yazd province using the Iranian model of desertification potential assessment (IMDPA) is one of the main objectives of this research. …”
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Spatial-temporal load prediction of electric bus charging station based on S2TAT by Guangnian Xiao, Hailin Tong, Yaqing Shu, Anning Ni

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ED-SA-ConvLSTM: A Novel Spatiotemporal Prediction Model and Its Application in Ionospheric TEC Prediction by Yalan Li, Haiming Deng, Jian Xiao, Bin Li, Tao Han, Jianquan Huang, Haijun Liu

“…Existing work based on Convolutional Long Short-Term Memory (ConvLSTM) primarily relies on convolutional operations for spatial feature extraction, which are effective at capturing local spatial correlations, but struggle to model long-range dependencies, limiting their predictive performance. …”
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Exploring the Role of Digital Economy in Energy Optimization of Manufacturing Industry Under the Constraint of Carbon Reduction? Based on Spatial Panel Threshold Hybrid Model by Lingyao Wang, Taofeng Wu, Fangrong Ren

“…Based on panel data from 30 provinces in mainland China from 2016 to 2022, this research investigates the spatial spillover effect and nonlinear impact of the digital economy on the energy optimization of the manufacturing industry using the spatial econometric and panel threshold model. …”
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Terrain Simplification Algorithm in Radio Wave Propagation Prediction by Dan Shi, Zhen Zhang, Fengshuo Wei, Cheng Lian

“…The spatial visibility algorithm and the probability-based power propagation model can be applied to the complex electromagnetic environment to analyze the influence of terrain simplification on prediction accuracy. …”
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Modelling and Using Spatial Effects in Nationwide Historical Data Improve Genomic Prediction of Rice Heading Date in Japan by Shoji Taniguchi, Takeshi Hayashi, Hiroshi Nakagawa, Kei Matsushita, Hiromi Kajiya-Kanegae, Jun-Ichi Yonemaru, Akitoshi Goto

“…To improve the prediction accuracy of models using historical data, we incorporated a spatial model to account for spatial structures among field stations, in addition to conventional genomic prediction models. …”
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Development of a river dissolved oxygen prediction model integrating spatial effects and multiple deep learning algorithm by Yubo Zhao, Mo Chen

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Modeling the Spatial Flows of Nitrogen: The Case of Xiamen by Yanmin Li, Tianqi Zhang, Shihang Wang, Yu-Sheng Shen, Shenghui Cui

“…Taking Xiamen as its research case, this study utilizes grid technology and spatial analysis to build a detailed spatial model for nitrogen flow at the grid scale. …”
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Modeling spatial interaction networks of the gut microbiota by Xiaocang Cao, Ang Dong, Guangbo Kang, Xiaoli Wang, Liyun Duan, Huixing Hou, Tianming Zhao, Shuang Wu, Xinjuan Liu, He Huang, Rongling Wu

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Machine learning based risk analysis and predictive modeling of structure fire related casualties by Andres Schmidt, Eric Gemmil, Russ Hoskins

“…As the first statewide analysis of its kind, our spatial assessment provides a useful tool for resource allocation, risk factor reduction, and safety education efforts targeted to reduce the number of serious injuries or fatalities from structure fires.…”
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Modeling the spatial distribution of anthrax in southern Kenya. by Fredrick Tom Otieno, John Gachohi, Peter Gikuma-Njuru, Patrick Kariuki, Harry Oyas, Samuel A Canfield, Jason K Blackburn, M Kariuki Njenga, Bernard Bett

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Modeling Spatial and Temporal Changes in Land-Uses and Land Cover of the Urmia Lake Basin Applying Cellular Automata and Markov Chain by Hadi Eskandari Damaneh, Hamid Gholami, Hassan Khosravi, Rasoul Mahdavi Najafabadi, Asadollah Khoorani, Gimmy Li

“…After the integrated CA-Markov approach assessed the model, the land-use maps were predicted for the years 2028 and 2038. …”
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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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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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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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Identifying Informative Nodes in Attributed Spatial Sensor Networks Using Attention for Symbolic Abstraction in a GNN-based Modeling Approach by Leonid Schwenke, Stefan Bloemheuvel, Martin Atzmueller

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Temperature-Dependent Modeling and Spatial Predictions for Identifying Geographical Areas in Brazil Suitable for the Use of <i>Cordyceps javanica</i> in Whitefly Control by Heloiza A. Boaventura, Lidiane A. Queirós, José Francisco A. Silva, Tarryn A. Goble, Kelly Pazolini, Allan F. Marciano, Eliane D. Quintela

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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&#x2013;land boundaries; the multilevel residual module, which enhances the model&#x2019;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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