An Ensemble Learning Model Considering Multiple Spatial Proximities and Its Application in the Study of Dust Storm Source Area Susceptibility on the Mongolian Plateau

The Mongolian Plateau is one of the primary sources of dust storms globally, and these storms significantly impact the climate, ecology, and human health in East Asia and beyond. Accurately identifying the source areas of dust storms is crucial for improving disaster prevention, mitigation, and environmental governance. This paper proposes an innovative geospatial heterogeneous ensemble learning model (sp-GSH_EL) that incorporates multiple spatial proximity measures. By combining Euclidean distance with great-circle distance, this model effectively integrates geographically weighted regression (GWR), geographically optimal similarity (GOS), and random forest (RF) models, taking into account spatial heterogeneity, global similarity, and non-linear relationships. Using dust storm occurrence data from the Mongolian Plateau between 2000 and 2021, the study introduces a novel method for identifying dust storm sources by combining aerosol optical depth (AOD) and dust storm occurrence data. By setting an AOD threshold and combining the hourly change rate of AOD, the model accurately captures the dynamic changes of dust source points. These source and non-source points are then used as training samples for dynamic identification of the susceptibility of dust storm source areas in the Mongolian Plateau. The results show that the overall accuracy of the sp-GSH_EL model was 0.941, with a precision of 0.929, recall rate of 0.954, F1–score of 0.941, MCC of 0.883, and AUC of 0.983, all outperforming traditional models. The study highlights that temperature, snow depth, and wind speed are key factors influencing dust storm source areas in the Mongolian Plateau. Additionally, it was found that the main source areas of dust storms are located in the Gobi Desert and arid steppe regions in the central and southern parts of the Plateau, with a significant expansion of source areas between 2005 and 2010. The total area of high-risk and very high-risk zones increased from 3.4% to 9.9%.

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Journal
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Published
2025-01-01
DOI
https://doi.org/10.1109/jstars.2025.3602836
Primary Topic
Meteorological Phenomena and Simulations
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article
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article

An Ensemble Learning Model Considering Multiple Spatial Proximities and Its Application in the Study of Dust Storm Source Area Susceptibility on the Mongolian Plateau

Mingyue Li, Dashtseren Avirmed, Daoting Zhang, Duwala et al.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Meteorological Phenomena and Simulations
article

An Ensemble Learning Model Considering Multiple Spatial Proximities and Its Application in the Study of Dust Storm Source Area Susceptibility on the Mongolian Plateau

Mingyue Li, Dashtseren Avirmed, Daoting Zhang, Duwala, Zhimin Hong, Yun He
article en

Abstract

The Mongolian Plateau is one of the primary sources of dust storms globally, and these storms significantly impact the climate, ecology, and human health in East Asia and beyond. Accurately identifying the source areas of dust storms is crucial for improving disaster prevention, mitigation, and environmental governance. This paper proposes an innovative geospatial heterogeneous ensemble learning model (sp-GSH_EL) that incorporates multiple spatial proximity measures. By combining Euclidean distance with great-circle distance, this model effectively integrates geographically weighted regression (GWR), geographically optimal similarity (GOS), and random forest (RF) models, taking into account spatial heterogeneity, global similarity, and non-linear relationships. Using dust storm occurrence data from the Mongolian Plateau between 2000 and 2021, the study introduces a novel method for identifying dust storm sources by combining aerosol optical depth (AOD) and dust storm occurrence data. By setting an AOD threshold and combining the hourly change rate of AOD, the model accurately captures the dynamic changes of dust source points. These source and non-source points are then used as training samples for dynamic identification of the susceptibility of dust storm source areas in the Mongolian Plateau. The results show that the overall accuracy of the sp-GSH_EL model was 0.941, with a precision of 0.929, recall rate of 0.954, F1–score of 0.941, MCC of 0.883, and AUC of 0.983, all outperforming traditional models. The study highlights that temperature, snow depth, and wind speed are key factors influencing dust storm source areas in the Mongolian Plateau. Additionally, it was found that the main source areas of dust storms are located in the Gobi Desert and arid steppe regions in the central and southern parts of the Plateau, with a significant expansion of source areas between 2005 and 2010. The total area of high-risk and very high-risk zones increased from 3.4% to 9.9%.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingVol. 18
Mongolian Academy of Sciences (MN), Inner Mongolia Normal University (CN), Chinese Academy of Agricultural Sciences (CN), Institute of Grassland Research (CN), Inner Mongolia University of Technology (CN)
Climate action
Openalex Percentile: Top 73%
Meteorological Phenomena and Simulations
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