Control strategies in the spatial population dynamics of Aedes aegypti vector using sterile mosquitoes and insecticides by Carmen Alicia Ramírez Bernate, Héctor Jairo Martínez Romero, Diana Marcela Erazo Borja

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Spatiotemporal prediction of soil organic carbon density in Europe (2000–2022) using earth observation and machine learning by Xuemeng Tian, Sytze de Bruin, Rolf Simoes, Mustafa Serkan Isik, Robert Minarik, Yu-Feng Ho, Murat Şahin, Martin Herold, Davide Consoli, Tomislav Hengl

“…Prediction accuracy varies by land cover, depth interval and year of prediction with the worst accuracy for shrubland and deeper soils 100–200 cm. …”
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From Prediction to Explanation: Using Explainable AI to Understand Satellite-Based Riot Forecasting Models by Scott Warnke, Daniel Runfola

“…This study investigates the application of explainable AI (XAI) techniques to understand the deep learning models used for predicting urban conflict from satellite imagery. …”
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Predicting ecotopes from hydrodynamic model data: Towards an ecological assessment of nature-based solutions by Soesja Brunink, Gijs G. Hendrickx

“…Quantifying the current ecological state and future ecological shifts faces challenges, including variable dependencies, spatial-temporal disparities, and the limitations in available information. …”
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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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Prediction of litchi flower induction in South China region based on the CMIP6 climate model by HOU Wei, ZHANG Liuhong, ZHANG Lei, LUAN Lan, ZHANG Mingjie, WANG Xiuzhen, ZHANG Hui

“…Additionally, we selected the average ensemble of four climate models (CanESM5, FGOALS-g3, GFDL-CM4, and IPSL-CM6A-LR) from CMIP6 to assess the spatial and temporal evolution characteristics of the commercial cultivation limits and flower formation induction of litchi under two climate scenarios, comparing the base period with future projections in the South China region. …”
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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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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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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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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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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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Unraveling the Deformation and Water Storage Characteristics of Different Aquifer Groups by Integrating PS-InSAR Technology and a Spatial Correlation Model by Danni Zhao, Beibei Chen, Huili Gong, Kunchao Lei, Chaofan Zhou, Jinming Hu

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