Evaluating the Uncertainty and Predictive Performance of Probabilistic Models Devised for Grade Estimation in a Porphyry Copper Deposit by Raymond Leung, Alexander Lowe, Arman Melkumyan

“…Probabilistic models are used to describe random processes and quantify prediction uncertainties in a principled way. …”
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Information-Theoretic Modeling of Categorical Spatiotemporal GIS Data by David Percy, Martin Zwick

“…An NLCD tool reports how much change occurred for each category of land use; for the study area examined, the most dynamic class is Evergreen Forest (EFO), so the presence or absence of EFO in 2021 was chosen as the dependent variable that our data modeling attempts to predict. RA predicts the outcome with approximately 80% accuracy using a sparse set of cells from a spacetime data cube consisting of neighboring lagged-time cells. …”
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High-Resolution Mapping of Litter and Duff Fuel Loads Using Multispectral Data and Random Forest Modeling by Álvaro Agustín Chávez-Durán, Miguel Olvera-Vargas, Inmaculada Aguado, Blanca Lorena Figueroa-Rangel, Ramón Trucíos-Caciano, Ernesto Alonso Rubio-Camacho, Jaqueline Xelhuantzi-Carmona, Mariano García

“…Our modeling approach allows us to estimate the continuous high-resolution spatial distribution of litter and duff fuel loads, aligned with their ecological context, which dictates their dynamics and spatial variability. …”
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Generalizable Storm Surge Risk Modeling by Mahlon Scott, Hsin-Hsiung Huang

“…Inspired by principles of robust statistical modeling, this paper introduces a Bayesian hierarchical model integrated with Gaussian processes to account for spatial random effects. …”
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Ecological risk assessment and prediction of riparian zones in the Jiangsu section of the Yangtze River from a spatiotemporal perspective by Zihan Zhu, Cheng Zhang, Yangyang Lu, Jian Ye, Guohua Fang, Changran Sun, Yun Yang

“…Focusing on the Yangtze River riparian zone in Jiangsu Province, an ecological risk assessment system with 20 indicators was developed based on a systematic analysis of the ecological risk exposure–response process. The temporal and spatial characteristics of ecological risks in the riparian zone from 2003 to 2023 were analysed using the ecological risk composite index model, Moran index, and Getis-Ord Gi* cold hotspot analysis method, while ecological risks for 2028 and 2033 were predicted using the Grey–Markov chain and PLUS models. …”
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Valuating the Effect of Toulmin Argumentation Model on Critical Thinking and Epistemological Beliefs in Online Education by Salih GÜLEN, İSMAIL DÖNMEZ

“…This study investigates the effectiveness of the Toulmin argumentation model in developing critical thinking skills and epistemological beliefs concerning the internet in an online educational setting, especially during the global coronavirus pandemic. …”
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Generating a 30 m Hourly Land Surface Temperatures Based on Spatial Fusion Model and Machine Learning Algorithm by Qin Su, Yuan Yao, Cheng Chen, Bo Chen

“…In this study, focusing on Chengdu city, a framework combining a spatiotemporal fusion model and machine learning algorithm was proposed and applied to retrieve hourly high spatial resolution LST data from Chinese geostationary weather satellite data and multi-scale polar-orbiting satellite observations. …”
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Spatial Evaluation of <i>Salurnis marginella</i> Occurrence According to Climate Change Using Multiple Species Distribution Models by Jae-Woo Song, Jaho Seo, Wang-Hee Lee

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Geometric Investigation of Al-Wind Dam Reservoir Northeastern Iraq, using Digital Elevation Models and Spatial Analyses System by Sabbar A. Saleh, Iktifaa T. Abdul Qadir, Amin M. Ibrahim, Huda M. Hussain

“… Geometric analysis of Al-Wind dam reservoir in Diyala discussed in this paper as necessary and strategic subject, spatial analysis systems were used to extract the area of Al-Wind dam reservoir from the digital elevations model (DEM), at 26 selected water levels in the reservoir with one meter interval, from 195 up to 219.5 m.a.s.l., the geometric criteria used to ​​extract the essential negative geometric elements represented by the Negative Volume (NV) Negative Planner Area (NPA) and Negative Surface Area (NSA), the perimeter of water body, the depth of water column and the shape factor of the reservoir, as well as for the positive geometric elements as Positive Volume (PV), Positive Planner Area (PPA) and Positive Surface Area (PSA) of the islands within the perimeter of the reservoir. …”
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Comparison of stochastic and deterministic models for gambiense sleeping sickness at different spatial scales: A health area analysis in the DRC. by Christopher N Davis, Ronald E Crump, Samuel A Sutherland, Simon E F Spencer, Alice Corbella, Shampa Chansy, Junior Lebuki, Erick Mwamba Miaka, Kat S Rock

“…The spatial heterogeneity in cases is reflected in modelling results, where we predict that under the current intervention strategies, the health area of Kinzamba II, which has approximately one third of the health zone's cases, will have the latest expected year for EoT. …”
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AI-powered simulation-based inference of a genuinely spatial-stochastic gene regulation model of early mouse embryogenesis. by Michael Alexander Ramirez Sierra, Thomas R Sokolowski

“…It also provides a framework for future exploration of similar spatial-stochastic systems in developmental biology.…”
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Within-Field Temporal and Spatial Variability in Crop Productivity for Diverse Crops—A 30-Year Model-Based Assessment by Ixchel Manuela Hernández-Ochoa, Thomas Gaiser, Kathrin Grahmann, Anna Maria Engels, Frank Ewert

“…The results revealed that the spatial variability in crop yield was higher than the temporal variability for most crops, except for sunflower. …”
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Analysis of Spatial Suitable Habitats of Four Subspecies of <i>Hippophae rhamnoides</i> in China Based on the MaxEnt Model by Mengyao He, Fanyan Ma, Junjie Ding, Panxin Niu, Cunkai Luo, Mei Wang, Ping Jiang

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Evaluation of Spatial Matching Between Water and Soil Resources in Shiyang River Basin Based on FLUS-InVEST Model by HOU Hui-min, WANG Hui, WANG Peng-quan, CAO Jin-jun

“…[Methods] Using the FLUS model, this study simulated the spatial patterns of land use of the Shiyang River Basin in 2035 under three scenarios: cropland protection, natural development, and ecological conservation. …”
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Spatial and Temporal Variability of Chlorophyll-a and the Modeling of High-Productivity Zones Based on Environmental Parameters: a Case Study for the European Arctic Corridor by Kuzmina Sofia, Lobanova Polina, Chepikova Svetlana Sergeevna

“…Our study aims to create models that predict the position of high chlorophyll-a concentration (Chl-a) zones in the European Arctic Corridor (the Barents, Norwegian and Greenland Seas) to monitor these changes. …”
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Examination of map reading skills with orienteering activity: An example of Many Facet Rasch Model by Şeyma Uyar, Hidayet Zünber, Onur Yayla

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Effect of phosphorus fractions on benthic chlorophyll-a: Insight from the machine learning models by Yuting Wang, Sangar Khan, Zongwei Lin, Xinxin Qi, Kamel M. Eltohamy, Collins Oduro, Chao Gao, Paul J. Milham, Naicheng Wu

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Temporal and Spatial Dynamics of Rodent Species Habitats in the Ordos Desert Steppe, China by Rui Hua, Qin Su, Jinfu Fan, Liqing Wang, Linbo Xu, Yuchuang Hui, Miaomiao Huang, Bobo Du, Yanjun Tian, Yuheng Zhao, Manduriwa

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Assessing the ecological risk and its driving forces on Islands using the Pressure-State-Response model by Meina Xu, Xiaochen Huang, Jiajun Li, Shaojun Qi, Ye Zhang, Xiaojie Zhang

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Predicting the Distribution of Mesophotic Coral Ecosystems in the Chagos Archipelago by Clara Diaz, Kerry L. Howell, Kyran P. Graves, Adam Bolton, Phil Hosegood, Edward Robinson, Nicola L. Foster

“…The goals of this study are to (1) predict the spatial distribution and extent of distinct benthic communities and MCEs in the Chagos Archipelago, central Indian Ocean, (2) test the effectiveness of a range of environmental and topography derived variables to predict the location of MCEs around Egmont Atoll and the Archipelago, and (3) independently validate the models produced. …”
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