FibroRegNet: A Regression Framework for the Pulmonary Fibrosis Prognosis Prediction Using a Convolutional Spatial Transformer Network by Pardhasaradhi Mittapalli, V. Thanikaiselvan

“…Predicting the growth of idiopathic pulmonary fibrosis (IPF) is crucial for effectively treating patients affected by the disease. …”
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Characteristics of Spatial and Temporal Evolution of Coastal Wetland Landscape Patterns and Prediction Analysis—A Case Study of Panjin Wetland, China by Qian Cheng, Ruixin Chen, Wei Xu, Meiqing Wang

“…For this research, we quantified the landscape type changes in Panjin Wetland from 1992–2022, and analyzed the interaction between the combined PLUS and InVEST models to predict the future evolution of spatial and temporal patterns of habitat quality (HQ) and landscape patterns in Panjin Wetland. …”
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Integration of fuzzy logic and geographic weighted regression modeling for enhanced groundwater potential mapping using remote sensing and GIS by Junguang Gao, Amnah A. Alasgah, Imran Ahmad, Faten Nahas, Mithas Ahmad Dar, Martina Zelenakova, Milashu Sisay, Getanew Sewnet Zewdu

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Learning behavior aware features across spaces for improved 3D human motion prediction by Ruiya Ji, Chengjie Lu, Zhao Huang, Jianqi Zhong

“…Additionally, we design an Euclidean Kinematic-Aware Extractor utilizing temporal-wise Kinematic-Aware Attention and spatial-wise Kinematic-Aware Feature Extraction. These two modules enhance and complement each other, leading to effective human motion prediction. …”
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Improving Discharge Predictions in Ungauged Basins: Harnessing the Power of Disaggregated Data Modeling and Machine Learning by Aggrey Muhebwa, Colin J. Gleason, Dongmei Feng, Jay Taneja

“…Abstract Current machine learning methods for discharge prediction often employ aggregated basin‐wide hydrometeorological data (lumped modeling) for parametric and non‐parametric training. …”
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Spatial risk modelling of highly pathogenic avian influenza in France: Fattening duck farm activity matters. by Jean Artois, Timothée Vergne, Lisa Fourtune, Simon Dellicour, Axelle Scoizec, Sophie Le Bouquin, Jean-Luc Guérin, Mathilde C Paul, Claire Guinat

“…In this study, we present a comprehensive analysis of the key spatial risk factors and predictive risk maps for HPAI infection in France, with a focus on the 2016-17 and 2020-21 epidemic waves. …”
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Where to refine spatial data to improve accuracy in crop disease modelling: an analytical approach with examples for cassava by Yevhen F. Suprunenko, Christopher A. Gilligan

“…However, the underlying data on spatial locations of host crops that are susceptible to a pathogen are often incomplete and inaccurate, thus reducing the accuracy of model predictions. …”
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Spatial and temporal distribution of infiltration, curve number and runoff coefficients using TOPMODEL and SCS-CN models by Mohammad Hossein Pishvaei, Shabnam Noroozpour, Touraj Sabzevari, Mostafa Akbari Kheirabadi, Andrea Petroselli

“…Infiltration, the process by which water enters the soil, is intricately intertwined with the attributes of the catchment, including soil composition and vegetation cover, both of which exhibit temporal and spatial variability. Accurate quantification of infiltration rates is imperative for enhancing the predictive capabilities of rainfall-runoff models, especially in regions with limited hydrological monitoring infrastructure, such as many developing countries where a significant portion of catchments remains ungauged. …”
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Mapping and understanding the regional farmland SOC distribution in southern China using a Bayesian spatial model by Bifeng Hu, Yibo Geng, Hanjie Ni, Zhou Shi, Zheng Wang, Nan Wang, Jipeng Luo, Modian Xie, Qian Zou, Thomas Optiz, Hongyi Li

“…Finally, an interpretable machine learning model, the SHapley Additive exPlanation (SHAP), is used to quantify the environmental covariates’ contribution to mapping SOC, as well as mapping spatial varying primary covariates for predicting SOC in the study area. …”
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Multi-scenario modelling of urban spatial growth under water resources and aquatic ecological environmental constraints by Ran Xu, Lu Liu, Yaliang Liu, Xin Yi, Hui Qiu

“…A logistic model based on spatial autocorrelation can explain the driving factors of land use in the study area. …”
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Ultra-short-term Multi-region Power Load Forecasting Based on Spearman-GCN-GRU Model by Junying WU, Xin LU, Hong LIU, Bin ZHANG, Shouliang CHAI, Yunchun LIU, Jianan WANG

“…To improve the prediction accuracy of multi-region power load, an ultra-short-term multi-region power load forecasting model based on Spearman-GCN-GRU is proposed with focus on the spatial-temporal correlation analysis of multi-region power data. …”
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Spatial transcriptomics and scRNA-seq: decoding tumor complexity and constructing prognostic models in colorectal cancer by Wei Song, Yatao Wang, Min Zhou, Fengqin Guo, Yanliang Liu

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Global foot-and-mouth disease risk assessment based on multiple spatial analysis and ecological niche model by Qi An, Yiyang Lv, Yuepeng Li, Zhuo Sun, Xiang Gao, Hongbin Wang

“…A multi-algorithm ensemble model considering climatic, geographic, and social factors was developed to predict the suitability area for FMDV, and then risk maps of FMD for each species of livestock were generated in combination with the distribution of livestock. …”
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Hybrid modeling of adsorption process using mass transfer and machine learning techniques for concentration prediction by Jing Lv, Lei Wang

“…Abstract This study presents a comprehensive hybrid modeling framework that integrates computational fluid dynamics (CFD) with machine learning (ML) techniques to predict chemical concentration distributions during the adsorption of organic compounds onto porous materials. …”
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Using Structural Class Pairing to Address the Spatial Mismatch Between GEDI Measurements and NFI Plots by Nikola Besic, Sylvie Durrieu, Anouk Schleich, Cedric Vega

“…The main challenge is the lack of systematic spatial alignment between GEDI footprints and National Forest Inventory (NFI) plots, which is necessary to accurately link in situ forest measurements with GEDI data. …”
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Numerical modeling of electromagnetic wave propagation in spatially-varying evaporation duct conditions via 3D parabolic equation method by Hanjie Ji, Hanjie Ji, Lixin Guo, Yan Zhang, Tianhang Nie, Yiwen Wei, Jinpeng Zhang, Qingliang Li, Xiangming Guo, Yusheng Zhang

“…Conventional two-dimensional (2D) models assume homogeneous refractive index distribution along the cross-range dimension in a single propagation plane, limiting their ability to capture the 3D spatial heterogeneities present in real-world scenarios. …”
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An Interpretable Implicit-Based Approach for Modeling Local Spatial Effects: A Case Study of Global Gross Primary Productivity Estimation by S. Du, H. Huang, K. Shen, Z. Liu, S. Tang

“…In geographic machine learning tasks, conventional statistical learning methods often struggle to capture spatial heterogeneity, leading to unsatisfactory prediction accuracy and unreliable interpretability. …”
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Predictive Deep Learning for High‐Dimensional Inverse Modeling of Hydraulic Tomography in Gaussian and Non‐Gaussian Fields by Quan Guo, Ming Liu, Jian Luo

“…In this work, we develop a novel method called HT‐INV‐NN, which combines dimensionality reduction techniques with a predictive deep learning (DL) model to estimate high‐dimensional Gaussian and non‐Gaussian channel fields. …”
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Design and development of an efficient RLNet prediction model for deepfake video detection by Varad Bhandarkawthekar, T. M. Navamani, Rishabh Sharma, K. Shyamala

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Adaptive dynamic prediction model of mining subsidence aided by measured data by Yuanfei Chen, Jianfeng Zha, Lei Wang

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