The precipitous decline of a gray fox population by Max R. Larreur, Clayton K. Nielsen, Damon B. Lesmeister, Guillaume Bastille-Rousseau

“…We then developed three predictive occupancy models that allowed comparison of gray fox spatial patterns and occupancy estimates over time. …”
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A dynamic adaptive graph convolutional recurrent network model for efficient mid-short term prediction of global sea surface salinity by Guangwen Peng, Yingbing Liu, Cong Xiao, Wenying Du, Changjiang Xiao

“…AGCRUs dynamically construct topological relationships via graph convolution to model spatial variations, while GRUs capture temporal dependencies. …”
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A Novel Forest Dynamic Growth Visualization Method by Incorporating Spatial Structural Parameters Based on Convolutional Neural Network by Linlong Wang, Huaiqing Zhang, Kexin Lei, Tingdong Yang, Jing Zhang, Zeyu Cui, Rurao Fu, Hongyan Yu, Baowei Zhao, Xianyin Wang

“…In this article, uneven-aged Chinese fir (<italic>Cunninghamia lanceolata</italic>) plantations were chosen as our study subject and proposed a novel method of forest dynamic growth visualization modeling by incorporating spatial structure parameters and using convolutional neural network technique (FDGVM-CNN-SSP) to explore the effect of spatial structure on the morphological growth and to develop a prediction growth model of Chinese fir plantations by introducing a convolutional neural network (CNN) model. …”
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From Field to Model: Determining EROSION 3D Model Parameters for the Emerging Biomass Plant <i>Silphium perfoliatum</i> L. to Predict Effects on Water Erosion Processes by Tobias Koch, Peter Aartsma, Detlef Deumlich, Peter Chifflard, Kerstin Panten

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Monitoring Spatial-Temporal Variability of Vegetation Coverage and Its Influencing Factors in the Yellow River Source Region from 2000 to 2020 by Boyang Wang, Jianhua Si, Bing Jia, Xiaohui He, Dongmeng Zhou, Xinglin Zhu, Zijin Liu, Boniface Ndayambaza, Xue Bai

“…The influencing factors on vegetation coverage were quantitatively analyzed using a geographic detector, and future tendencies in vegetation coverage were predicted utilizing the Future Land Use Simulation (FLUS) model. …”
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A Spatial–Temporal Analysis and Multi-Scenario Projections of Carbon Sequestration in Sea Islands: A Case Study of Pingtan Island by Siyu Chen, Ming Xu, Heshan Lin, Fei Tang, Jinyan Xu, Yikang Gao, Yunling Zhuang, Yong Chen

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Bayesian feedback in the framework of ecological sciences by Mario Figueira, Xavier Barber, David Conesa, Antonio López-Quílez, Joaquín Martínez-Minaya, Iosu Paradinas, Maria Grazia Pennino

“…This paper focuses on a sequential Bayesian procedure for linking two models by updating prior distributions. The Bayesian paradigm is implemented together with the integrated nested Laplace approximation (INLA) methodology, which is an effective approach for making inference and predictions in spatial models with high performance and low computational cost. …”
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Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins by Senlin Tang, Fubao Sun, Wenbin Liu, Hong Wang, Yao Feng, Ziwei Li

“…Abstract Streamflow prediction in ungauged basins (PUB) is challenging, and Long Short‐Term Memory (LSTM) is widely used to for such predictions, owing to its excellent migration performance. …”
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Advances in spatiotemporal multiscale water-energy balance models: A Review by DONG Weijie, LIU Zhiyong, CHEN Xiaohong

“…Water and energy balance models have become increasingly important for simulating hydrological and thermal processes, predicting runoff, and managing water resources over the past few decades. …”
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Prediction of Temperature Distribution with Deep Learning Approaches for SM1 Flame Configuration by Gökhan Deveci, Özgün Yücel, Ali Bahadır Olcay

“…The second framework employs a U-Net-based convolutional neural network enhanced by an RGB Fusion preprocessing technique, which integrates multiple scalar fields from non-reacting (cold flow) conditions into composite images, significantly improving spatial feature extraction. The training and validation processes for both models were conducted using 80% of the CFD data for training and 20% for testing, which helped assess their ability to generalize new input conditions. …”
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Exploring the Spatiotemporal Heterogeneity of Stream Nitrogen Concentrations in a Typical Human‐Activity‐Influenced Headwater Watershed in South China by Congsheng Fu, Haixia Zhang, Huawu Wu, Haohao Wu, Yang Cao, Ye Xia, Zichun Zhu

“…This study thoroughly explored the spatiotemporal variations in stream nitrogen concentrations in a typical headwater watershed in South China. Spatially distributed measurements were conducted during 2020–2022, and mathematical modeling was implemented based on incorporating these data. …”
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CA-STIM: an interpolation model with spatio-temporal evolution characteristics and cross-attention mechanism for 2D island morphology sequences by Peng Zhang, Wenzhou Wu, Shaochen Shi, Fengyu Li, Fenzhen Su

“…Using three coral islands in the South China Sea (Beizi, Mahuan, and Xiyue) as case studies, we perform 2D spatial morphology series interpolation. Experimental results demonstrate that our model outperforms baseline methods, achieving Dice scores of 0.9681, 0.9675, and 0.975 and Intersection-over-Union (IOU) scores of 0.9383, 0.9373, and 0.9513 on Beizi, Mahuan, and Xiyue Island, respectively.…”
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Modeling Wetland Biomass and Aboveground Carbon: Influence of Plot Size and Data Treatment Using Remote Sensing and Random Forest by Tássia Fraga Belloli, Diniz Carvalho de Arruda, Laurindo Antonio Guasselli, Christhian Santana Cunha, Carina Cristiane Korb

“…This study examined how different sample data treatments and plot sizes impact a random forest model’s performance based on RS for AGB and Corg prediction. …”
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Human-based metaheuristics and non-parametric learning for groundwater-prone area mapping by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Seyedeh Zeinab Shogrkhodaei, Biswajeet Pradhan, Soo-Mi Choi

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Pulsed Focused Nonlinear Acoustic Fields from Clinically Relevant Therapeutic Sources in Layered Media: Experimental Data and Numerical Prediction Results by Tamara KUJAWSKA

“…The comparison of the experimental results with those simulated numerically has shown that the model based on the TAWE approach predicts well both the spatial-peak and spatial-spectral pressure variations in the pulsed focused nonlinear beams produced by the transducer used in water for all excitation levels complying with the condition corresponding to weak or moderate source-pressure levels. …”
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Shared and distinct neural signatures of feature and spatial attention by Anmin Yang, Jinhua Tian, Wenbo Wang, Liqin Zhou, Ke Zhou

“…The debate on whether feature attention (FA) and spatial attention (SA) share a common neural mechanism remains unresolved. …”
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A novel ST-iTransformer model for spatio-temporal ambient air pollution forecasting by Rui Zhang, Norhashidah Awang

“…Additionally, ablation experiments confirm that the spatio-temporal embedding, the inclusion of spatial data, and the addition of meteorological data all improve the prediction accuracy of the model. …”
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Using extreme gradient boosting for predictive urban expansion analysis in Rustenburg, South Africa from 2000 to 2030 by Paidamwoyo Mhangara, Eskinder Gidey, Bruce Steadman Mayise

“…This study modeled the spatial extent of urban growth in Rustenburg from 1994 to 2022 using Extreme Gradient Boosting (XGB) and predicted future urban expansion from 2022 to 2030 through the Cellular Automata Simulation in the MOLUSCE plugin. …”
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Reconstructed hyperspectral imaging for in-situ nutrient prediction in pine needles by Yuanhang Li, Yuanhang Li, Jun Du, Jun Du, Chuangjie Zeng, Chuangjie Zeng, Yongshan Wu, Yongshan Wu, Junxian Chen, Junxian Chen, Teng Long, Teng Long, Yongbing Long, Yongbing Long, Yubin Lan, Yubin Lan, Xiaoliang Che, Tianyi Liu, Jing Zhao, Jing Zhao

“…However, its high cost and complexity hinder practical field applications.MethodsTo overcome these limitations, we propose a deep-learning-based method to reconstruct hyperspectral images from RGB inputs for in situ needle nutrient prediction. The model reconstructs hyperspectral images with a spectral range of 400–1000 nm (3.4 nm resolution) and spatial resolution of 768×768. …”
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Toward a Multi‐Representational Approach to Prediction and Understanding, in Support of Discovery in Hydrology by Luis A. De la Fuente, Hoshin V. Gupta, Laura E. Condon

“…Specifically, we test a lumped water‐balance model (GR4J), a data‐based dynamical systems model (LSTM), and a data‐based regression tree model (Random Forest). …”
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