Predicting fine-scale downstream migratory movement of Atlantic salmon smolt (Salmo salar) in front of a hydropower plant by Kim M. Bærum, Ana T. Silva, Henrik Baktoft, Karl Ø. Gjelland, Finn Økland, Torbjørn Forseth

“…We present a spatially explicit individual-based model for predicting the movement of Atlantic salmon smolts in regulated rivers in Norway, parameterised for smolt movements in the River Mandal and the River Orkla. …”
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Research on short-term traffic flow prediction based on the PCC-IGA-LSTM model by Junxi Zhang, Shiru Qu, Yang Bi, Lijing Ma

“…To effectively address the spatial–temporal feature mining problem in short-term traffic flow prediction for complex road networks, a new method that combined the Pearson correlation coefficient (PCC) and improved genetic algorithm to optimize the long short-term memory model (IGA-LSTM) was constructed. …”
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ASSESSMENT OF ROMANIAN ALPINE HABITATS SPATIAL SHIFTS BASED ON CLIMATE CHANGE PREDICTION SCENARIOS by ADRIAN CONSTANTINESCU, JENICĂ HANGANU, ANTHONY LEHMANN, NICOLAS RAY

“…Maxent and BIOCLIM were used to create spatial distribution models for Mesophilous oligotrophic mountain pasture and Subalpine oligotrophic pastures. …”
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Prediction of spatial yield strength distribution in Al–Mg–Sc alloy fabricated by coaxial laser wire directed energy deposition by Xianyue Liu, Yuelan Di, Gang Wang, Longcen Ji, Yining Yuan, Yiming Rong

“…The spatially heterogeneous thermal history during additive manufacturing (AM) leads to variations in the mechanical properties of the fabricated parts. …”
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Spatial-Similarity Dynamic Graph Bidirectional Double-Cell Network for Traffic Flow Prediction by Zhifei Yang, Zeyang Li, Jia Zhang

“…This research advances traffic prediction methodologies through its integrated approach to dynamic spatial correlation modeling and bidirectional temporal learning, providing valuable insights for intelligent transportation system development.…”
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Integrating machine learning and spatial clustering for malaria case prediction in Brazil’s Legal Amazon by Kayo Henrique de Carvalho Monteiro, Élisson da Silva Rocha, Luis Augusto Morais, Elton Gino Santos, Sebastião Rogerio da S. Neto, Vanderson Sampaio, Patricia Takako Endo

“…The integration of K-means clustering further improved the model predictive accuracy by accounting for spatial heterogeneity and capturing localized transmission dynamics. …”
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Single-nucleotide polymorphism analysis accurately predicts multiple impairments in hippocampal activity and memory performance in a murine model of idiopathic autism by Isabel Barón-Mendoza, Luis A. Márquez, Aliesha González-Arenas, Jessica Guzmán-Condado, Vladimir A. Martínez-Rojas, Johaly Anguiano-Buenfil, Montserrat Mejía-Hernández, Jorge Luis Almazán, Leonor Pérez-Martínez, Gustavo Pedraza-Alva, Emilio J. Galván, Angélica Zepeda

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Modeling to predict cases of hantavirus pulmonary syndrome in Chile. by Elaine O Nsoesie, Sumiko R Mekaru, Naren Ramakrishnan, Madhav V Marathe, John S Brownstein

“…Several studies have estimated risk based on spatial and temporal distribution of cases in relation to climate and environmental variables, but few have considered climatological modeling of HPS incidence for monitoring and forecasting purposes.…”
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Enhancing urban air quality prediction using time-based-spatial forecasting framework by Shrikar Jayaraman, Nathezhtha T, Abirami S, Sakthivel G

“…The outcomes demonstrate the TBS model’s ability to accurately predict AQI values. …”
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Autonomous Driving Decision-Making Method Based on Spatial-Temporal Fusion Trajectory Prediction by Yutao Luo, Aining Sun, Jiawei Hong

“…In this paper, we propose a driving strategy learning method based on spatial-temporal feature prediction. Firstly, the spatial interaction between vehicles is implicitly modeled using a graph convolutional neural network and multi-head attention mechanism, and the gated loop unit is embedded to capture the sequential temporal relationship to establish a prediction model incorporating spatial-temporal features. …”
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Self-adaptive spatial-temporal network based on heterogeneous data for air quality prediction by Feng Chang, Liang Ge, Siyu Li, Kunyan Wu, Yaqian Wang

“…However, accurately predicting future air quality remains a challenging task because of the complex spatial-temporal dependencies of air quality. …”
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Dynamic Spatial–Temporal Graph Neural Network for Cooling Capacity Prediction in HVDC Systems by Hao Sun, Shaosen Li, Jianxiang Huang, Hao Li, Guanxin Jing, Ye Tao, Xincui Tian

“…Traditional machine learning methods, while effective in static scenarios, struggle to capture these dependencies, and existing deep learning models often lack the ability to jointly model spatial and temporal relationships. …”
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Exploration of geo-spatial data and machine learning algorithms for robust wildfire occurrence prediction by Svetlana Illarionova, Dmitrii Shadrin, Fedor Gubanov, Mikhail Shutov, Usman Tasuev, Ksenia Evteeva, Maksim Mironenko, Evgeny Burnaev

“…The goal of this study is to explore the potential of predicting wildfire occurrences using various available environmental parameters - meteorological, geo-spatial, and anthropogenic - and machine learning (ML) algorithms. …”
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Predicting spatial spread of rabies in skunk populations using surveillance data reported by the public. by Kim M Pepin, Amy J Davis, Daniel G Streicker, Justin W Fischer, Kurt C VerCauteren, Amy T Gilbert

“…We developed a dynamic patch-occupancy model which predicts spatio-temporal spreading while accounting for heterogeneous sampling. …”
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Transformer based spatially resolved prediction of mechanical properties in wire arc additive manufacturing by Mohammad Keshmiri, Shirin Dehgahi, Abdullah Mohiuddin, Ahmed J. Qureshi

“…The results demonstrate that the framework achieves good prediction capabilities using a small dataset. It provides a state-of-the-art methodology for predicting the spatial and temporal evolution of mechanical properties leveraging the transformer architecture. …”
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Integrative spatial and single-cell transcriptomics elucidate programmed cell death-driven tumor microenvironment dynamics in hepatocellular carcinoma by Kai Lei, Kai Lei, Yutong Zhao, Shumin Li, Jiawei Liu, Wenhao Chen, Caihong Zhou, Yi Zhang, Jinmei Tan, Jian Wu, Qi Zhou, Qi Zhou, Jiehui Tan

“…This study aims to develop a PCD scores prediction model to evaluate the prognosis of hepatocellular carcinoma (HCC) and elucidate the tumor microenvironment differences.MethodsWe analyzed transcriptomic data from 363 HCC patients in the TCGA database and 221 patients in the GEO database to develop a PCD prediction model. …”
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Modeling Spatial Data with Heteroscedasticity Using PLVCSAR Model: A Bayesian Quantile Regression Approach by Rongshang Chen, Zhiyong Chen

“…We apply a Bayesian quantile regression (BQR) of the partially linear varying coefficient spatial autoregressive (PLVCSAR) model for spatial data to improve the prediction of performance. …”
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Modeling spatial distributions of Amah Mutsun priority cultural plants to support Indigenous cultural revitalization by Annalise Taylor, Alexii Sigona, Maggi Kelly

“…We utilized community science datasets with an ensemble modeling approach that combines the results of five machine learning models to predict not only the distribution of each species, but also the relative certainty of those predictions spatially. …”
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Predicting the effect of landscape structure on epidemic invasion using an analytical estimate for infection rate by Yevhen F. Suprunenko, Stephen J. Cornell, Christopher A. Gilligan

“…We explore the potential of using an analytical approximation for the rate, [Formula: see text], at which susceptible crop fields become infected at the start of an epidemic to predict the effect that the spatial structure of a host landscape will have on an epidemic. …”
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Disease prediction models and operational readiness. by Courtney D Corley, Laura L Pullum, David M Hartley, Corey Benedum, Christine Noonan, Peter M Rabinowitz, Mary J Lancaster

“…As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4), spatial (26), ecological niche (28), diagnostic or clinical (6), spread or response (9), and reviews (3). …”
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