Towards biologically realistic estimates of home range and spatial exposure for colonial animals by Holly I. Niven, Jana W. E. Jeglinski, Geert Aarts, Ewan D. Wakefield, Jason Matthiopoulos

“…We propose a new HR estimation method for colonial animals that accounts for such spatially complex interactions without computationally expensive individual‐based modelling (IBM). …”
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Lightning Prediction in the Tehran Region Using the WRF Model With Multiple Physical Parameterizations and an Ensemble Approach by Sakineh Khansalari, Maryam Gharaylou

“…The initial and boundary conditions for the WRF model were derived from the Global Forecast System data set, with a spatial resolution of 0.5°. …”
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A Hybrid Spatiotemporal Deep Learning Model for Short-Term Metro Passenger Flow Prediction by Hao Zhang, Jie He, Jie Bao, Qiong Hong, Xiaomeng Shi

“…A hybrid spatiotemporal deep learning model is developed to predict both inbound and outbound passenger flows for every 10 minutes. …”
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A comparative approach of machine learning models to predict attrition in a diabetes management program. by Samantha Kanny, Grisha Post, Patricia Carbajales-Dale, William Cummings, Janet Evatt, Windsor Westbrook Sherrill

“…These findings underscore the difficulty for models to accurately predict health behavior outcomes, highlighting the need for future research to improve predictive modeling to better support patient engagement and retention.…”
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Time series prediction based on the variable weight combination of the T-GCN-Luong attention and GRU models by Yushu Guo, Jiacheng Huang, Xuchu Jiang

“…The results revealed that (1) the inclusion of spatial information significantly improved the effectiveness of the temperature predictions. (2) The Luong attention mechanism weights different time steps and improves the prediction accuracy of the T-GCN model. (3) The TGLAG combination model constructed via the variable weight method exhibited good predictive performance at 15 sites. …”
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Predicting suitable habitats and conservation areas for Suaeda salsa using MaxEnt and Marxan models by Yongji Wang, Zhusong Liu, Kefan Wu, Jiamin Peng, Yanyue Mao, Guanghua Zhao, Fenguo Zhang

“…Using 130 occurrence records and 14 selected environmental variables, this study applied the MaxEnt model to predict suitable habitats of S. salsa across China under current and future climate scenarios. …”
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Development of an enhanced base unit generation framework for predicting demand in free‐floating micro‐mobility by Dohyun Lee, Kyoungok Kim

“…Although these methods are feasible and provide a uniform area division, they are highly susceptible to the Modifiable Areal Unit Problem (MAUP), which is a critical issue in spatial data analysis. Although MAUP can adversely affect predictive model learning, studies addressing this issue are scarce. …”
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ResHAN-GAM: A novel model for the inversion and prediction of soil organic matter content by Liying Cao, Dongjie Yin, Miao Sun, Yuzhu Yang, Musharaf Hassan, Yunpeng Duan

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DBSCAN-PCA-INFORMER-Based Droplet Motion Time Prediction Model for Digital Microfluidic Systems by Zhijie Luo, Bin Zhao, Wenjin Liu, Jianhua Zheng, Wenwen Chen

“…As chip usage frequency rises, device degradation introduces seasonal and trend patterns in droplet motion time data, complicating predictive modeling. This paper first employs the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm to analyze the droplet motion time data in digital microfluidic systems. …”
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Vehicle Lane Change Multistep Trajectory Prediction Based on Data and CNN_BiLSTM Model by Shijie Gao, Zhimin Zhao, Xinjian Liu, Yanli Jiao, Chunyang Song, Jiandong Zhao

“…In order to accurately predict the lane-changing trajectory of the vehicle and improve the driving safety of the vehicle, a lane-changing trajectory prediction model based on the combination of convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) neural network is proposed by comprehensively considering the historical driving behavior, the spatial characteristics of surrounding vehicles and the bidirectional time sequence information of the vehicle trajectory. …”
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Prediction of runoff in the upper reaches of the Hei River based on the LSTM model guided by physical mechanisms by Huazhu Xue, Chaoqiang Guo, Guotao Dong, Chenchen Zhang, Yaokang Lian, Qian Yuan

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Construction of crown profile prediction model of Pinus yunnanensis based on CNN-LSTM-attention method by Longfeng Deng, Jianming Wang, Jiting Yin, Yuling Chen, Baoguo Wu

“…However, existing modeling approaches face limitations in capturing the crown’s spatial heterogeneity and vertical structure. …”
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Predicting changes in land use and land cover using remote sensing and land change modeler by Brijmohan Bairwa, Rashmi Sharma, Arnab Kundu, Saad Sh. Sammen, Fahad Alshehri, Chaitanya Baliram Pande, Chaitanya Baliram Pande, Zoltan Orban, Ali Salem, Ali Salem

“…The integration of geo-spatial and remote sensing technologies is pivotal in comprehending these dynamics and formulating strategies for future natural resource management. …”
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Modeling the Horizontal Velocity Field of the Earth’s Crust in a Regular Grid from GNSS Measurements by Manevich Aleksandr, Losev Ilya, Avdonina Alina, Shevchuk Roman, Kaftan Vladimir, Tatrinov Victor

“…Spatial modeling based on a neural network approach allows for the adequate modeling of the field of recent crustal movements and deformations of the Earth’s crust beyond the geodetic network contour. …”
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Spatio-Temporal Generalization of VIS-NIR-SWIR Spectral Models for Nitrogen Prediction in Sugarcane Leaves by Carlos Augusto Alves Cardoso Silva, Rodnei Rizzo, Marcelo Andrade da Silva, Matheus Luís Caron, Peterson Ricardo Fiorio

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Predicting the impact of climate change on the distribution of rhododendron on the qinghai-xizang plateau using maxent model by Sen-Xin Chai, Li-Ping Ma, Zhong-Wu Ma, Yu-Tian Lei, Ya-Qiong Ye, Bo Wang, Yuan-Ming Xiao, Ying Yang, Guo-Ying Zhou

“…To investigate the possible spatial distribution of Rhododendron on the Qinghai-Xizang Plateau in light of future global warming scenarios, we employed the Maximum entropy model (MaxEnt model) to map its suitable habitat using geographic distribution data and environmental factors projected for 2050s and 2070s, considering three representative concentration pathway (RCP) scenarios, while identifying the key factors influencing their distribution. …”
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Predicting bone metastasis risk of colorectal tumors using radiomics and deep learning ViT model by Guanfeng Chen, Wenxi Liu, Yingmin Lin, Jie Zhang, Risheng Huang, Deqiu Ye, Jing Huang, Jieyun Chen

“…The ViT model’s strength lies in its ability to capture complex spatial relationships and long-range dependencies within the imaging data, which are often missed by traditional models. …”
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Enhanced real-time Parkinson’s disease monitoring and severity prediction using a multi-faceted deep learning approach by Marreddy Naga Sabari, Deepak Ch

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The impact of spatiotemporal variability of environmental conditions on wheat yield forecasting using remote sensing data and machine learning by Keltoum Khechba, Mariana Belgiu, Ahmed Laamrani, Alfred Stein, Abdelhakim Amazirh, Abdelghani Chehbouni

“…This study aims to assess the impact of spatial and temporal heterogeneity of environmental conditions on wheat yield forecasting using machine learning models. …”
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Testing spatial transferability of species distribution models reveals differing habitat preferences for an endangered delphinid (Cephalorhynchus hectori) in Aotearoa, New Zealand by Steph Bennington, Peter W. Dillingham, Scott D. Bourke, Stephen M. Dawson, Elisabeth Slooten, William J. Rayment

“…Abstract Species distribution models (SDMs) can be used to predict distributions in novel times or space (termed transferability) and fill knowledge gaps for areas that are data poor. …”
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