A Multi-Spatial-Scale Ocean Sound Speed Profile Prediction Model Based on a Spatio-Temporal Attention Mechanism by Shuwen Wang, Ziyin Wu, Shuaidong Jia, Dineng Zhao, Jihong Shang, Mingwei Wang, Jieqiong Zhou, Xiaoming Qin

“…Moreover, in terms of ocean sound speed, most of these models predict an ocean sound speed profile (SSP) at a single coordinate position, and only a few predict multi-spatial-scale SSPs. …”
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Intermodel and method comparison of mean radiant temperature from numerical weather prediction models: Evaluation of enhanced spatial resolution in Europe by Oleh SKRYNYK, Pavol NEJEDLÍK, Krzysztof BŁAŻEJCZYK

“…Mean Radiant Temperature (MRT), derivable from numerical weather prediction (NWP) models, is a critical input for many such indices. …”
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Prediction of the spatial distribution of vine weevil under climate change using multiple variable selection methods by Ga-Young Kim, Wang-Hee Lee

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Machine Learning Traffic Flow Prediction Models for Smart and Sustainable Traffic Management by Rusul Abduljabbar, Hussein Dia, Sohani Liyanage

“…Specifically, this paper evaluates the impact of multisource sensor inputs and spatial detector interactions on machine learning-based traffic flow prediction. …”
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Machine learning based risk analysis and predictive modeling of structure fire related casualties by Andres Schmidt, Eric Gemmil, Russ Hoskins

“…As the first statewide analysis of its kind, our spatial assessment provides a useful tool for resource allocation, risk factor reduction, and safety education efforts targeted to reduce the number of serious injuries or fatalities from structure fires.…”
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Advanced AI techniques for landslide susceptibility mapping and spatial prediction: A case study in Medellín, Colombia by I.N. Gómez-Miranda, C. Restrepo-Estrada, A. Builes-Jaramillo, João Porto de Albuquerque

“…This study presents a novel landslide susceptibility model that incorporates spatial and temporal dependencies, including landslide recurrence. …”
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Pedestrian trajectory prediction model based on self-supervised spatiotemporal graph network by Shiji Yang, Xuezhong Xiao

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A machine learning-based prediction-to-map framework for rapid and accurate spatial flood prediction by Daoyang Bao, Z. George Xue, Matthew Hiatt, Kehui Xu, Courtney K. Harris, Jill C. Trepanier

“…Trained on observed data and numerical model outputs, P2M delivers rapid, accurate spatial flood predictions. …”
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Attributing the spatial-temporal contributions of inflow nitrogen and phosphorus loads to lake water quality and insights for effective mitigation strategies by Zhilong Zhao, Bo Yao, Yu Li, Zhaokui Ni, Shengrui Wang

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Performance analysis of rank reduction estimator in the presence of unexpected modeling errors by WANG Ding, WU Ying

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Predicting of Temporal-Spatial Sand Dunes Transition Caused by Marine Storms (Case Study: The Coast of Makran, Iran) by Soleiman PirouzZadeh, Mahmood Khosravi, Samad Fotohi

“…The aim of this paper is  modeling and prediction of changes in  land-use in 2035 by using  CA Markov model and Landsat satellite images in the West of Zarabad,( The coasts of Makran). …”
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Spatial Prediction of Soil Continuous and Categorical Properties Using Deep Learning Approaches for Tamil Nadu, India by Thamizh Vendan Tarun Kshatriya, Ramalingam Kumaraperumal, Sellaperumal Pazhanivelan, Nivas Raj Moorthi, Dhanaraju Muthumanickam, Kaliaperumal Ragunath, Jagadeeswaran Ramasamy

“…With machine learning models being the most utilized modeling technique for digital soil mapping (DSM), the implementation of model-based deep learning methods for spatial soil predictions is still under scrutiny. …”
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Spatial-Temporal Coordination of Agricultural Quality and Water Carrying Capacity in Chengdu-Chongqing by Bingchang Li, Xinlan Liang, Cuihua Bian, Fengxin Sun, Zichen Xia, Binghao Sun, Ying Cao

“…Employing the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) model, obstacle degree analysis, standard deviational ellipse, and grey prediction modeling, the study finds that AQI exhibits a sustained upward trend—doubling in over half of the region’s cities—while WCI shows fluctuating growth, constrained by climatic extremes and uneven water distribution. …”
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On Hierarchical Bayesian Spatial Small Area Model for Binary Data under Spatial Misalignment by Kindie Fentahun Muchie, Anthony Kibira Wanjoya, Samuel Musili Mwalili

“…Model-based Bayesian analysis is popular for its ability to combine information from several sources as well as taking account uncertainties in the analysis and spatial prediction of spatial data. …”
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Spatial modeling and ecological suitability of Ebola virus disease in Africa. by Lombo Baluma Didier, Lukusa Lumu Jude, Esuka Igabuchia Franck, HaoNing Wang, Xiao-Long Wang

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Generation of Spatial Structure of Urban Parks Based on Spatial Analysis of Agent-Based Models by Huizi KONG, Tianming LIU, Jiajie LIAO, Liu CUI

“…By adjusting model parameters, the foraging paths of slime molds in the designed site are simulated, and their spatial function distribution is analyzed. …”
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Spatial heterogeneity and spatial bias analyses in hedonic price models: some practical considerations by Khalid Haniza

“…Estimation of a hedonic price function using Malaysian dataset of agricultural land sale values indicates spatial disaggregation and spatial dependence. However, diagnostic tests and actual estimation of spatial models do not always provide unambiguous conclusions while predicted errors do not vary all that much from those generated by simpler models. …”
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The Impact Prediction of Income Tax Standards on Company Performance: A Hybrid Spatial Artificial Intelligence Approach by Sawsan Kareem Abdullah

“…In this study, various artificial intelligence methods such as artificial neural networks, support vector machines, deep learning, decision trees, random forests, and genetic algorithms were used in combination with spatial modeling. The results show that income tax accounting standards have a significant impact on the financial performance of companies, and the combination of artificial intelligence methods with spatial modeling significantly increases the prediction accuracy. …”
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Can spatial distribution of ungulates be predicted by modeling camera trap data related to landscape indices?... by Antonio Belda, Sandra Oltra-Crespo, Pau Miró-Martínez, Benito Zaragozí

“…In this work, we aimed at finding the best model to predict the distribution pattern of wildlife and to explain the relationship between environmental conditions with the species detected by camera traps. …”
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Orthogonal intercellular signaling for programmed spatial behavior by Paul K Grant, Neil Dalchau, James R Brown, Fernan Federici, Timothy J Rudge, Boyan Yordanov, Om Patange, Andrew Phillips, Jim Haseloff

“…We used this model to predict optimal expression levels for receiver proteins, to create an effective two‐channel cell communication device. …”
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