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Impact of data spatial resolution on barley yield prediction mapping
Published 2025-12-01“…The choice of spatial resolution plays a key role in model performance, as the resolution of data input influences prediction quality. …”
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A Bayesian Prediction Spatial Model for Confirmed Dengue Cases in the State of Chiapas, Mexico
Published 2022-01-01“…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
Published 2025-05-01“…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
Published 2021-01-01“…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
Published 2025-07-01“…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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Conditional logistic individual-level models of spatial infectious disease dynamics
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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.
Published 2013-01-01“…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
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Modelling and predicting biogeographical patterns in river networks
Published 2016-04-01“…I show that biomass and abundance of host fish are a likely explanation for the autocorrelation in mussel abundance within a 15-km spatial extent. The application of universal kriging with the empirical model enabled precise prediction of mussel abundance within segments of river networks, something that has the potential to inform conservation biogeography. …”
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Ephemeral gullies. A spatial and temporal analysis of their characteristics, importance and prediction
Published 2002-06-01“…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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Modeling the Spatial Distribution of Wildfire Risk in Chile Under Current and Future Climate Scenarios
Published 2025-03-01Subjects: Get full text
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Exploring spatial machine learning techniques for improving land surface temperature prediction
Published 2024-07-01Subjects: Get full text
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How spatial scales enhance prediction: an interpretable multi-scale framework for bike-sharing demand prediction
Published 2025-07-01“…However, most studies focus only on one specific spatial scale, thus ignoring the inter-scale synergy improvement on prediction performance. …”
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Spatial Modeling of Auto Insurance Loss Metrics to Uncover Impact of COVID-19 Pandemic
Published 2025-04-01Subjects: “…spatial model…”
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Landslide spatial prediction using data-driven based statistical and hybrid computational intelligence algorithms
Published 2025-12-01“…Optimization of landslide susceptibility model driven by geological environment: a key challenge for disaster reduction in mountainous areas. …”
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Towards a global spatial machine learning model for seasonal groundwater level predictions in Germany
Published 2025-08-01“…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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Applying Machine Learning Algorithms for Spatial Modeling of Flood Susceptibility Prediction over São Paulo Sub-Region
Published 2025-05-01Subjects: “…flood spatial modeling…”
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Rainfall-induced Landslide Susceptibility Prediction Considering Spatial Heterogeneity
Published 2025-07-01“…ObjectiveLandslides represent a severe and frequent natural hazard, posing significant threats to human life and property. Current models for predicting landslide susceptibility exhibit two primary limitations: the inability to fully capture the spatial heterogeneity of environmental factors such as terrain, soil, and vegetation, and the failure to accurately distinguish between landslides induced by extreme and non-extreme rainfall events. …”
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