Machine learning-based prediction of sand and dust storm sources in arid Central Asia

Figures & data

Figure 1. Geographical location of the study area and spatial distribution of the main deserts in ACA.

Figure 2. Flowchart of this study in the GEE platform. See Table 1 for detailed land/climate variables.

Figure 3. Thematic maps of SDS source effective predictor variables and RGB image. (a) DLSTR. (b) Vol_Wt_S. (c) LCT. (d) Wt_C. (e) NDVI. (f) So_Sa. (g) Slope. (h) So_Wt. (i) Sur_R. (j) Wd_Sp. (k) Sn_C. (l) A_Temp. (m) T_Prec. (n) So_Temp. (o) RGB image.

Table 1. Summary of the input variables in this study.

Category	Variable	Spatial Resolution	Temporal Resolution	Temporal Coverage	Source/Reference
Terrestrial factors	DLSTR	1000 m	daily	2003–2020	AGCD (Li et al. 2018)
	Vol_Wt_S	11132 m	hourly	1982 to present	ERA5 (Hersbach et al. 2018)
	LCT	500 m	yearly	2001 to present	MCD12Q1 (Friedl and Sulla-Menashe 2019)
	Wt_C	30 m	yearly	1984–2021	JRC_GSW (Pekel et al. 2016)
	NDVI	250 m	16-Day	2000 to present	MOD13Q1 (Didan 2021)
	So_Sa	250 m	-	-	OpenLandMap (Hengl 2018)
	Slope	90 m	-	-	SRTM_DEM (Jarvis et al. 2008)
	So_Wt	250 m	-	-	OpenLandMap (Hengl and Gupta 2019)
	Sur_R	90 m	-	-	Geomorpho90 m (Amatulli et al. 2020)
Climatic factors	Wd_Sp	11132 m	hourly	1982 to present	ERA5 (Hersbach et al. 2018)
	Sn_C	11132 m	hourly	1982 to present	ERA5 (Hersbach et al. 2018)
	A_Temp	11132 m	hourly	1982 to present	ERA5 (Hersbach et al. 2018)
	T_Prec	11132 m	hourly	1982 to present	ERA5 (Hersbach et al. 2018)
	So_Temp	11132 m	hourly	1982 to present	ERA5 (Hersbach et al. 2018)

Table 2. Performance evaluation of four ML based models.

ML Models	TA	VA	Precision	Recall	F1 Score	AUCROC	AUCPR
RF	0.998	0.875	0.811	0.889	0.847	0.949	0.918
SVM	0.886	0.860	0.793	0.862	0.824	0.920	0.877
GBT	0.994	0.856	0.793	0.866	0.827	0.931	0.887
MaxEnt	0.872	0.859	0.772	0.888	0.825	0.934	0.897

Figure 4. ROC curves for the four methods in SDS source susceptibility prediction based on nine model iterations.

Figure 5. PR curves for the four methods in SDS source susceptibility prediction based on nine model iterations.

Figure 6. SDS source susceptibility maps produced via the ML-based methods in spring. (a) 2008-4-29, (b) 2016-5-11, (c) 2018-4-18.

Figure 7. SDS source susceptibility maps produced using the ML-based methods in summer and autumn. (d) 2010-8-26, (e) 2015-6-10, (f) 2019-9-24.

Figure 8. SDS source susceptibility maps produced using the ML-based methods in winter. (g) 2007-3-1, (h) 2013-1-23, (i) 2019-1-2.

Figure 9. Relative importance of the variables to the prediction model outputs based on the random forest.

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