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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Predicting urban landslides in the hilly regions of Bangladesh leveraging a hybrid machine learning model and CMIP6 climate projections by Md․ Ashraful Islam, Musabbir Ahmed Arrafi, Mehedi Hasan Peas, Tanvir Hossain, Md Mehedi Hasan, Sanzida Murshed, Monira Jahan Tania

“…The model was trained using diverse geospatial parameters including topographical, hydrological, soil, and geological parameters, along with an updated landslide inventory, enabling spatially explicit predictions of landslide susceptibility. …”
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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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The Role of Landscape Metrics and Spatial Processes in Performance Evaluation of GEOMOD (Case Study: Neka River Basin) by Shrif Joorabian Shooshtari, Kamran Shayesteh, Mehdi Gholamalifard, Mahmood Azari, Juan Ignacio López-Moreno

“…The relative error obtained by comparison of observed map versus simulated map for patch density, related circumscribing circle, and for effective mesh size metrics was the highest. The model was able to predict shape complexity, fragmentation, compactness and spatial heterogeneity, and area of forest class with high consistency. …”
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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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Testing the Applicability and Transferability of Data-Driven Geospatial Models for Predicting Soil Erosion in Vineyards by Tünde Takáts, László Pásztor, Mátyás Árvai, Gáspár Albert, János Mészáros

“…Predictions used spatially exhaustive, auxiliary, and environmental covariables. …”
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Conditional logistic individual-level models of spatial infectious disease dynamics by Tahmina Akter, Rob Deardon

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Prediction of the Spatial Distribution of Petrophysical Properties of Sediment Formations Using Multidimensional Splines by V. V. Lapkovsky, V. A. Kontorovich, K. I. Kanakova, S. E. Ponomareva, B. V. Lunev

“…The results can be computed for individual wells as for inter-well space, allowing for the creation of geological cross-sections of predicted properties and 3D models of their distribution. …”
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Predictive Deep Learning for High‐Dimensional Inverse Modeling of Hydraulic Tomography in Gaussian and Non‐Gaussian Fields by Quan Guo, Ming Liu, Jian Luo

“…In this work, we develop a novel method called HT‐INV‐NN, which combines dimensionality reduction techniques with a predictive deep learning (DL) model to estimate high‐dimensional Gaussian and non‐Gaussian channel fields. …”
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Applying Machine Learning Algorithms for Spatial Modeling of Flood Susceptibility Prediction over São Paulo Sub-Region by Temitope Seun Oluwadare, Marina Pannunzio Ribeiro, Dongmei Chen, Masoud Babadi Ataabadi, Saba Hosseini Tabesh, Abiodun Esau Daomi

Subjects: “…flood spatial modeling…”
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Spatial modeling for predicting and identifying levels of climate-related disease hazards in Iraq using (GIS) Kirkuk as a model by Abdlwahd Jamel Alqdori Areej

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Integrated framework for assessment and spatial prediction of humus layer properties of forest soils by Felix Thomas, Carina Becker, Rainer Petzold, Karsten Schmidt, Thomas Scholten, Ulrike Werban

“…We tested the developed framework in a case study on a forest site in Saxony, investigating C/N ratio, pH value, cation exchange capacity and base saturation. Random Forest model calibration for spatial prediction achieved R2 > 0.9 for all investigated humus layer properties. …”
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A Predictive Spatial Model to Quantify the Risk of Air-Travel-Associated Dengue Importation into the United States and Europe by Lauren M. Gardner, David Fajardo, S. Travis Waller, Ophelia Wang, Sahotra Sarkar

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A framework for continual learning in real-time traffic forecasting utilizing spatial–temporal graph convolutional recurrent networks by Mariam Labib Francies, Abeer Twakol Khalil, Hanan M. Amer, Mohamed Maher Ata

“…To address these challenges, this research presents an innovative framework known as the Continual Learning-based Spatial–Temporal Graph Convolutional Recurrent Neural Network (STGNN-CL) for persistent and accurate long-term traffic flow prediction. …”
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Modeling the Spatial Distribution of Wildfire Risk in Chile Under Current and Future Climate Scenarios by John Gajardo, Marco Yáñez, Robert Padilla, Sergio Espinoza, Marcos Carrasco-Benavides

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An optimized spatial target trajectory prediction model for multi-sensor data fusion in air traffic management by Jian Dong, Yuan Xu, Rigeng Wu, Chengwang Xiao

“…In comparative analyses, the proposed network significantly outperforms prevailing trajectory prediction models across multiple dimensions. In this paper, we propose a new deep learning network, and apply it to the real-world engineering challenge of spatial target trajectory prediction in the air traffic management domain.…”
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Vit-Traj: A Spatial–Temporal Coupling Vehicle Trajectory Prediction Model Based on Vision Transformer by Rongjun Cheng, Xudong An, Yuanzi Xu

“…In recent years, data-driven vehicle trajectory prediction models have become a significant research focus, and various spatial–temporal neural network models, based on spatial–temporal data, have been proposed. …”
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Spatial-temporal deep learning model based on Similarity Principle for dock shared bicycles ridership prediction by Jiahui Zhao, Zhibin Li, Pan Liu, Mingye Zhang

“…The key challenge of traffic demand prediction lies in modeling the complex spatial dependencies and temporal dynamics. …”
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