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 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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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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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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Separable Reversible Data Hiding in Encrypted 3D Mesh Models Based on Spatial Clustering and Multi-MSB Prediction by Y. Mao, Y. Xu

“…Input model vertices are partitioned into exclusive clusters through spatial clustering, ensuring close proximity within each cluster. …”
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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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Machine learning based risk analysis and predictive modeling of structure fire related casualties by Andres Schmidt, Eric Gemmil, Russ Hoskins

“…Our results show that the age of victims, fire service response times, and availability of working smoke or fire detectors were among the most important parameters for predicting fatal outcomes of structure fires. Furthermore, a predictive Bayesian regularized neural network ensemble classifier was developed to model the severity of casualties and project a spatial risk classification on the census block level. …”
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Hydrodynamics Model Identification and Model-Based Control Application of a New Type of AUV by Lunyang Lin, Yuxiang Chen, Hong Xiong, Chunliang Yu, Hong Zhu, Yiyang Xing, Guodong Zheng

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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

“…Abstract Due to the rapid growth of unmanned aerial vehicles (UAVs), their spectrum resources become scarce, leading to UAVs requiring spectrum prediction to share the spectrum with other users. However, contemporary prediction methods may have difficulty in predicting the spectrum states at the next location, because the UAVs cannot obtain the historical data in advance to train prediction models. …”
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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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Estimating building height using scene classification and spatial geometry by Yonghua Jiang, Jingxin Chang, Yunming Wang, Shaodong Wei, Deren Li

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Leveraging Spectral Neighborhood Information for Corn Yield Prediction with Spatial-Lagged Machine Learning Modeling: Can Neighborhood Information Outperform Vegetation Indices? by Efrain Noa-Yarasca, Javier M. Osorio Leyton, Chad B. Hajda, Kabindra Adhikari, Douglas R. Smith

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Spatial change, time tendency and projection to HIV/AIDS incidence in an eastern province of China by Yiwei Li, Shijia Yuan, Yang Zhao, Xingguang Yang, Hongyu Li, Yanlin Lv, Wenhui Gao, Surong Zhao, Ronghang Liu, Chunlei Han

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InSAR-RiskLSTM: Enhancing Railway Deformation Risk Prediction with Image-Based Spatial Attention and Temporal LSTM Models by Baihang Lyu, Ziwen Zhang, Heinz D. Fill

“…To address these limitations, this study introduces InSAR-RiskLSTM, a novel framework that leverages the high-resolution and wide-coverage capabilities of Interferometric Synthetic Aperture Radar (InSAR) to enhance railway deformation risk prediction. The primary objective of this study is to develop an advanced predictive model that accurately captures both temporal dependencies and spatial susceptibilities in railway deformation processes. …”
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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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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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