Spatial Prediction Models and Applications.
نویسندگان
چکیده
منابع مشابه
Combining Spatial Models for Shallow Landslides and Debris-Flows Prediction
Mass movements in Brazil are common phenomena, especially during strong rainfall events that occur frequently in the summer season. These phenomena cause losses of lives and serious damage to roads, bridges, and properties. Moreover, the illegal occupation by slums on the slopes around the cities intensifies the effect of the mass movement. This study aimed to develop a methodology that combine...
متن کاملSpatial contextual classification and prediction models for mining geospatial data
Modeling spatial context (e.g., autocorrelation) is a key challenge in classification problems that arise in geospatial domains. Markov random fields (MRF) is a popular model for incorporating spatial context into image segmentation and land-use classification problems. The spatial autoregression (SAR) model, which is an extension of the classical regression model for incorporating spatial depe...
متن کاملSpatial prediction models for landslide hazards: review, comparison and evaluation
The predictive power of logistic regression, support vector machines and bootstrap-aggregated classification trees (bagging, double-bagging) is compared using misclassification error rates on independent test data sets. Based on a resampling approach that takes into account spatial autocorrelation, error rates for predicting “present” and “future” landslides are estimated within and outside the...
متن کاملOn estimation and prediction for spatial generalized linear mixed models.
We use spatial generalized linear mixed models (GLMM) to model non-Gaussian spatial variables that are observed at sampling locations in a continuous area. In many applications, prediction of random effects in a spatial GLMM is of great practical interest. We show that the minimum mean-squared error (MMSE) prediction can be done in a linear fashion in spatial GLMMs analogous to linear kriging. ...
متن کاملCombining Regressive and Auto-Regressive Models for Spatial-Temporal Prediction
A two-phased method for prediction in spatialtemporal domains is proposed. After an ordinary regression model is trained on spatial data, a prediction is adjusted by incorporating autoregressive modeling of residuals in time. The prediction accuracy of the proposed method is evaluated on simulated agricultural data with a significant improvement of accuracy for both linear and non-linear regres...
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ژورنال
عنوان ژورنال: GEOINFORMATICS
سال: 2001
ISSN: 1347-541X,0388-502X
DOI: 10.6010/geoinformatics.12.58