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Palabras clave – provistas por la editorial

artificial neural network; n/a; model switching; sensitivity analysis; neural networks; logit boost; Qaidam Basin; land subsidence; land use/land cover (LULC); naïve Bayes; multilayer perceptron; convolutional neural networks; single-class data descriptors; logistic regression; feature selection; mapping; particulate matter 10 (PM10); Bayes net; gray-level co-occurrence matrix; multi-scale; Logistic Model Trees; classification; Panax notoginseng; large scene; coarse particle; grayscale aerial image; Gaofen-2; environmental variables; variable selection; spatial predictive models; weights of evidence; landslide prediction; random forest; boosted regression tree; convolutional network; Vietnam; model validation; colorization; data mining techniques; spatial predictions; SCAI; unmanned aerial vehicle; high-resolution; texture; spatial sparse recovery; landslide susceptibility map; machine learning; reproducible research; constrained spatial smoothing; support vector machine; random forest regression; model assessment; information gain; ALS point cloud; bagging ensemble; one-class classifiers; leaf area index (LAI); landslide susceptibility; landsat image; ionospheric delay constraints; spatial spline regression; remote sensing image segmentation; panchromatic; Sentinel-2; remote sensing; optical remote sensing; materia medica resource; GIS; precise weighting; change detection; TRMM; traffic CO; crop; training sample size; convergence time; object detection; gully erosion; deep learning; classification-based learning; transfer learning; landslide; traffic CO prediction; hybrid model; winter wheat spatial distribution; logistic; alternating direction method of multipliers; hybrid structure convolutional neural networks; geoherb; predictive accuracy; real-time precise point positioning; spectral bands