Spatial Variables and Land Use Change Models: A Study on Conditioning Patterns of Natural Vegetation Suppression and Persistence by Macleidi Varnier, Eliseu José Weber

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The future of spatial epidemiology in the AI era: enhancing machine learning approaches with explicit spatial structure by Nima Kianfar, Benn Sartorius, Colleen L. Lau, Robert Bergquist, Behzad Kiani

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Ephemeral gullies. A spatial and temporal analysis of their characteristics, importance and prediction by Jeroen Nachtergaele, Jean Poesen, Gerard Govers

“…This study, therefore, aimed at:1) describing spatial and temporal variations in ephemeral gully characteristics, in three contrasting environments;2) extending the existing studies on the importance of ephemeral gully erosion in space and time by using high-altitude stereo aerial photos (HASAP) to assess ephemeral gully volumes;3) improving ephemeral gully prediction, through the development of both empirical relationships to directly predict ephemeral gully volumes and process-oriented relationships to be built in physically-based erosion models;4) evaluating the medium to long-term evolution of an (ephemeral) gully.…”
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Subglacial hydrology modeling predicts high winter water pressure and spatially variable transmissivity at Helheim Glacier, Greenland by Aleah Sommers, Colin Meyer, Mathieu Morlighem, Harihar Rajaram, Kristin Poinar, Winnie Chu, Jessica Mejia

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A Bayesian Prediction Spatial Model for Confirmed Dengue Cases in the State of Chiapas, Mexico by Manuel Solís-Navarro, Cruz Vargas-De-León, María Gúzman-Martínez, Josselin Corzo-Gómez

“…Therefore, this study aimed to develop and validate a simple Bayesian prediction spatial model for the state of Chiapas, Mexico. …”
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Global Horizontal Irradiance Prediction Model Based on Mixed Spatial Information and Aerosol Classification by XiuYan Gao, YuTian Hou, Suning Li, Yuan Yuan

“…This study aims to explore the impact of different types of aerosols on predicting GHI. First, we expanded the data within a fixed region by incorporating spatial information to supplement the timescale data. …”
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A Multifeatures Spatial-Temporal-Based Neural Network Model for Truck Flow Prediction by Shengyou Wang, Chunfu Shao, Yajiao Zhai, Song Xue, Yan Zheng

“…Therefore, in this paper, we focus on truck traffic flow and propose a Multifeatures Spatial-Temporal-Based Neural Network model (M-BiCNNGRU) to improve its prediction. …”
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Assessment of spatial autocorrelation and scalability in fine-scale wildfire random forest prediction models by Madeleine Pascolini-Campbell, Joshua B. Fisher, Kerry Cawse-Nicholson, Christine M. Lee, Natasha Stavros

“…We assessed the role of spatial autocorrelation in driving model performance by: (1) increasing the sample spacing of our dataset, (2) introducing new predictors that represent spatial structure in the data, and (3) training our model on half the fires and predicting the other half of the fires. …”
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Rainfall-induced Landslide Susceptibility Prediction Considering Spatial Heterogeneity by ZHANG Xingfu, JIANG Yuanjun, ABI Erdi

“…DEC-Based Clustering: The DEC model predicted higher landslide densities in high- and very-high-susceptibility zones by capturing spatial heterogeneity. …”
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Exploring spatial machine learning techniques for improving land surface temperature prediction by Arunab K.S., Mathew A.

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ED-SA-ConvLSTM: A Novel Spatiotemporal Prediction Model and Its Application in Ionospheric TEC Prediction by Yalan Li, Haiming Deng, Jian Xiao, Bin Li, Tao Han, Jianquan Huang, Haijun Liu

“…Existing work based on Convolutional Long Short-Term Memory (ConvLSTM) primarily relies on convolutional operations for spatial feature extraction, which are effective at capturing local spatial correlations, but struggle to model long-range dependencies, limiting their predictive performance. …”
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Introducing Spatial Heterogeneity via Regionalization Methods in Machine Learning Models for Geographical Prediction: A Spatially Conscious Paradigm by Lukas Boegl, Ourania Kounadi

“… This study addresses the challenge of incorporating spatial heterogeneity in predictive modeling by introducing regionalization methods in the preprocessing step of the modeling workflow. …”
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Analysis and prediction of infectious diseases based on spatial visualization and machine learning by Yunyun Cheng, Yanping Bai, Jing Yang, Xiuhui Tan, Ting Xu, Rong Cheng

“…In order to better apply the stacking model to the prediction of new infectious diseases, we applied the prediction model based on the COVID-19 dataset to the prediction of the number of AIDS and pulmonary tuberculosis (PTB) cases, and verified the wide applicability of our model in the prediction of infectious diseases.…”
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The use of bivariate spatial modeling of questionnaire and parasitology data to predict the distribution of Schistosoma haematobium in Coastal Kenya. by Hugh J W Sturrock, Rachel L Pullan, Jimmy H Kihara, Charles Mwandawiro, Simon J Brooker

“…This study investigates the use of bivariate spatial modelling of available and multiple data sources to predict the prevalence of S. haematobium at every school along the Kenyan coast.…”
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Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis by Rui Huang, Miao Liu, Yongmei Ding

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Landslide spatial prediction using data-driven based statistical and hybrid computational intelligence algorithms by Xia Zhao, Wei Chen, Paraskevas Tsangaratos, Ioanna Ilia, Qingfeng He

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A spatial predictive model for malaria resurgence in central Greece integrating entomological, environmental and social data. by Panagiotis Pergantas, Andreas Tsatsaris, Chrisovalantis Malesios, Georgia Kriparakou, Nikolaos Demiris, Yiannis Tselentis

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Terrain Simplification Algorithm in Radio Wave Propagation Prediction by Dan Shi, Zhen Zhang, Fengshuo Wei, Cheng Lian

“…The spatial visibility algorithm and the probability-based power propagation model can be applied to the complex electromagnetic environment to analyze the influence of terrain simplification on prediction accuracy. …”
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Towards a global spatial machine learning model for seasonal groundwater level predictions in Germany by S. Kunz, A. Schulz, M. Wetzel, M. Nölscher, T. Chiaburu, F. Biessmann, S. Broda

“…Global ML architectures enable predictions across numerous monitoring wells concurrently using a single model, allowing predictions over a broad range of hydrogeological and meteorological conditions and simplifying model management. …”
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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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