Multi-Task Learning-Based Traffic Flow Prediction Through Highway Toll Stations During Holidays by Xiaowei Liu, Yunfan Zhang, Zhongyi Han, Hao Qiu, Shuxin Zhang, Jinlei Zhang

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Spatial and temporal model for WQI prediction based on back-propagation neural network, application on EL MERK region (Algerian southeast) by Saber Kouadri, Samir Kateb, Rachid Zegait

“…The test shows that the model is suitable for predicting WQI with an error rate of 9.3%.…”
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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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InSAR-RiskLSTM: Enhancing Railway Deformation Risk Prediction with Image-Based Spatial Attention and Temporal LSTM Models by Baihang Lyu, Ziwen Zhang, Heinz D. Fill

“…To address these limitations, this study introduces InSAR-RiskLSTM, a novel framework that leverages the high-resolution and wide-coverage capabilities of Interferometric Synthetic Aperture Radar (InSAR) to enhance railway deformation risk prediction. The primary objective of this study is to develop an advanced predictive model that accurately captures both temporal dependencies and spatial susceptibilities in railway deformation processes. …”
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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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Exploring suicidal thoughts among prospective university students: a study with applications of machine learning and GIS techniques by Mohammed A. Mamun, Firoj Al-Mamun, Md Emran Hasan, Nitai Roy, Moneerah Mohammad ALmerab, David Gozal, Md. Shakhaoat Hossain

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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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Estimating building height using scene classification and spatial geometry by Yonghua Jiang, Jingxin Chang, Yunming Wang, Shaodong Wei, Deren Li

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Predictive quality of census-based socio-economic indicators on Covid-19 infection risk at a fine spatial scale in France by Nicolas Romain-Scelle, Benjamin Riche, Thomas Benet, Muriel Rabilloud

“…Ten census-based ecological covariates were evaluated as predictors of case incidence using a Poisson regression with conditional autoregressive (CAR) spatial effects. Benefits of CAR effects and covariates on model predictive ability was assessed comparing posterior predictive distribution of case incidence with the observed value for each statistical unit. …”
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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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Spatial change, time tendency and projection to HIV/AIDS incidence in an eastern province of China by Yiwei Li, Shijia Yuan, Yang Zhao, Xingguang Yang, Hongyu Li, Yanlin Lv, Wenhui Gao, Surong Zhao, Ronghang Liu, Chunlei Han

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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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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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Pedestrian trajectory prediction model based on self-supervised spatiotemporal graph network by Shiji Yang, Xuezhong Xiao

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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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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 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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