Spatial prediction method for dust storm source area susceptibility based on sp-gsh-el model

By using the sp-GSH-EL model, combined with aerosol optical thickness and rate of change to screen active dust sources, and by combining multiple covariates and ensemble learning algorithms, the limitations of coverage and insufficient handling of spatial heterogeneity in existing dust storm source area identification technologies have been solved, achieving more accurate prediction of dust storm source area susceptibility.

CN121074694BActive Publication Date: 2026-07-21INSTITUTE OF GRASSLAND RESEARCH OF CAAS
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Patents(China)
Current Assignee / Owner
INSTITUTE OF GRASSLAND RESEARCH OF CAAS
Filing Date
2025-06-30
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Existing technologies for identifying dust storm source areas suffer from limitations in the coverage of ground-based observation methods and untimely data updates. Remote sensing technology has also failed to effectively handle spatial heterogeneity, resulting in low modeling accuracy across multi-scale spatial data.

Method used

The sp-GSH-EL model was adopted to screen active dust sources by aerosol optical thickness (AOD) threshold and rate of change. Combined with multiple covariates and ensemble learning algorithms, spatial weighted ensemble neural network (SWENN) was used to optimize spatial heterogeneity and improve the dynamic monitoring and modeling accuracy of the model.

Benefits of technology

It enables dynamic monitoring and precise identification of dust storm source areas, improves the modeling accuracy and interpretability of multi-scale spatial data, and provides more accurate predictions of susceptibility to dust storm source areas.

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Abstract

The present application relates to the field of meteorological remote sensing, and specifically discloses a sandstorm source area susceptibility spatial prediction method based on an sp-GSH-EL model; including sandstorm source point extraction, non-sandstorm source point extraction, obtaining a covariant, defining a response variable, integrated learning model training, and sandstorm source area susceptibility prediction; by combining aerosol optical depth (AOD) threshold method and hourly AOD change rate, the dynamic change of dust source point can be more accurately captured, and the timeliness and accuracy of sandstorm source area monitoring can be improved. At the same time, it has the ability to capture spatial heterogeneity: the sp-GSH-EL model combines different spatial proximities to optimize the description ability of local heterogeneity, global similarity and non-linear relationship, and improve the modeling accuracy of multi-scale spatial data.
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