Prediction of the daily spatial variation of stem water potential in cherry orchards using weather and Sentinel-2 data by Francisco Zambrano, Abel Herrera, Mauricio Olguín, Miro Miranda, Jesica Garrido, Andrea Miyasaka Almeida

“…The primary goal of this work is to predict the daily spatial variation of Ψs using machine learning models. …”
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Spatial-temporal Differentiation and Prediction of Extreme Weather Events and Vegetation Cover in Guizhou Province Under Climate Change by LI Xi'nan, HE Linhong, XUE Lianqing

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Spatial predictions of soil moisture across a longitudinal gradient in semiarid ecosystems using UAV and RGB sensors by Alexander Hernandez, Efrain Duarte, Peter Porter, Holden Brecht

“…Texture metrics (‘mean’ and ‘entropy’), and the Excess Green (ExG) index had high predictive power while RGB bands performed poorly. Unlike Idaho and Montana, the spatial predictions for Utah and California showed high reliability (α < 0.01). …”
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Spatially explicit metrics improve the evaluation of species distribution models facing sampling biases by Claudio A. Bracho-Estévanez, Salvador Arenas-Castro, Juan P. González-Varo, Pablo González-Moreno

“…Furthermore, most predictions rely only on non-spatial metrics such as the AUC and the TSS to evaluate model performance. …”
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A Machine Learning Approach for Predicting Particle Spatial, Velocity, and Temperature Distributions in Cold Spray Additive Manufacturing by Lurui Wang, Mehdi Jadidi, Ali Dolatabadi

“…Stage 1 applies sampling and a K-nearest-neighbor kernel-density-estimation algorithm that predicts the spatial distribution of impacting particles and re-allocates weights in regions of under-estimation. …”
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Predicting Rural Industrial Transformation via Coupling Coordination Between Polder-Based Spatial Features and Industrial Development by Wenzhu Zhou, Dawei Wang, Yiwen Zhang, Hanjing Xu

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DGL-STFA: Predicting lithium-ion battery health with dynamic graph learning and spatial–temporal fusion attention by Zheng Chen, Quan Qian

“…This combination enables DGL-STFA to effectively model both dynamic spatial relationships and long-term temporal dependencies, enhancing SOH prediction accuracy. …”
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Mt or not Mt: Temporal variation in detection probability in spatial capture-recapture and occupancy models by Sollmann, Rahel

“…Here, I investigate whether closed spatial capture-recapture (SCR) and single season occupancy models are robust to ignoring temporal variation in detection probability. …”
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Correlation-regression analysis of modelled chlorine residual's spatial variability in water supply network by Sanja Marčeta, Dušan Prodanović

“…From this point of view the water quality request for minimum chlorinated and safe potable water is understandable. In numerable modeling and experimental research the spatial diversity of chlorine residual was found to correlate with the daily consumption schedule, the water temperature, the initial dose of chlorine and organic matter content in the water. …”
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Novel extensions to the Fisher copula to model flood spatial dependence over North America by D. A. Alexandre, C. Chaudhuri, J. Gill-Fortin

“…We propose novel extensions to the Fisher copula to statistically model the spatial structure of observed historical flood record data across North America. …”
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A Computing Model of Selective Attention for Service Robot Based on Spatial Data Fusion by Huanzhao Chen, Guohui Tian

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Automated Generation of Urban Spatial Structures Based on Stable Diffusion and CoAtNet Models by Dian Yu, Bo Wan, Qiang Sheng

“…We simultaneously trained two models: one is a LoRA Model based on the Stable Diffusion architecture used for generating road networks similar to those of various city road spatial structures; the other is a CoAtNet Model (Convolution + Transformer) used as an evaluation model to predict the space-syntax parameters of road structures and calculate the Mean Absolute Percentage Error (MAPE) relative to real urban samples. …”
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An Iterative Pixel-Based Dimensional Voting Model for High Spatial-Resolution Image Classification by Muhamad Asyraf Mohd Pouzi, Haza Nuzly Abdull Hamed, Muhammad Razib Othman, Hishammuddin Asmuni, Mohd Adham Isa, Husni Ruslai

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A high resolution spatial modelling framework for landscape-level, strategic conservation planning by T. Foxley, P. Lintott, S. Stonehouse, J. Flannigan, E.L. Stone

“…The aim of this study was to develop a spatial modelling framework for protecting biodiversity in the planning process. …”
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Advanced air quality prediction using multimodal data and dynamic modeling techniques by Umesh Kumar Lilhore, Sarita Simaiya, Rajesh Kumar Singh, Abdullah M. Baqasah, Roobaea Alroobaea, Majed Alsafyani, Afnan Alhazmi, M. D. Monish Khan

“…The attention mechanism directs the model’s focus to the most informative features, improving predictive accuracy. …”
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Displacement Patterns and Predictive Modeling of Slopes in the Bayan Obo Open-Pit Iron Mine by Penghai Zhang, Yang Li, Xin Dong, Tianhong Yang, Honglei Liu

“…The displacement time series were decomposed using Variational Mode Decomposition (VMD) into trend and periodic components, for which Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) models were respectively developed. The results indicate that (1) DBSCAN effectively detects clusters characterized by high average cumulative displacement and broad spatial distribution, while filtering out isolated outliers. (2) The trend component prediction achieved a coefficient of determination (R²) of 0.99755, while the periodic component prediction yielded a root mean square error (RMSE) of just 0.0978 mm. …”
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Spectroscopic analysis (UV-VIS-NIR) for predictive modeling of macro and micronutrients in grapevine leaves by J.I. Manzano, M. Rodríguez-Febereiro, M. Fandiño, M. Vilanova, J.J. Cancela

“…The ultraviolet (UV) range played a minor role, highlighting the predominant importance of the VIS-NIR regions in spectroscopic analyses.Finally, the results support the potential of this technique for swiftly and non-invasively predicting both macro and micronutrient levels in grapevine plants, and facilitate the fertilization planning using variety-specific reference levels, or precision viticulture adapted to site-specific demands, including spatial intra-plot variability.…”
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Spatial modeling of brine level and salinity in the Qarhan Salt Lake using GIS and automated machine learning algorithms by Dongmei Yu, Zitao Wang, Chao Yue, Jianping Wang

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Ada-GCNLSTM: An adaptive urban crime spatiotemporal prediction model by Miaoxuan Shan, Chunlin Ye, Peng Chen, Shufan Peng

“…In this paper, we introduce a novel deep learning-based model, adaptive-GCNLSTM (Ada-GCNLSTM). Specifically, in the spatial feature extraction module, we enhance the model's ability to capture crime spatial distributions by leveraging graph convolutional networks to model spatial dependencies in conjunction with the maximum mean discrepancy to extract the universal features of crime data. …”
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Wide-Area Active Frequency Control with Multi-Step-Size MPC by Yan Xie, Shiying Ma, Jiakai Shen, Xiaojun Tang, Jianmiao Ren, Liwen Zheng

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