Predicting of dust storm source by combining remote sensing, statistic-based predictive models and game theory in the Sistan watershed, southwestern Asia
Mahdi BOROUGHANI1,*(), Sima POURHASHEMI2, Hamid GHOLAMI3, Dimitris G KASKAOUTIS4,5
1Research Center for Geoscience and Social Studies, Hakim Sabzevari University, Sazbevar 9617976487, Iran 2Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar 9617976487, Iran 3Department of Natural Resources Engineering, University of Hormozgan, Hormozgan 7916193145, Iran 4Institute for Environmental Research and Sustainable Development, National Observatory of Athens, Athens 15784, Greece 5Environmental Chemical Processes Laboratory, Department of Chemistry, University of Crete, Crete 70013, Greece
Dust storms in arid and desert areas affect radiation budget, air quality, visibility, enzymatic activities, agricultural products and human health. Due to increased drought and land use changes in recent years, the frequency of dust storms occurrence in Iran has been increased. This study aims to identify dust source areas in the Sistan watershed (Iran-Afghanistan borders)-an important regional source for dust storms in southwestern Asia, using remote sensing (RS) and bivariate statistical models. Furthermore, this study determines the relative importance of factors controlling dust emissions using frequency ratio (FR) and weights of evidence (WOE) models and interpretability of predictive models using game theory. For this purpose, we identified 211 dust sources in the study area and generated a dust source distribution map-inventory map-by dust source potential index based on RS data. In addition, spatial maps of topographic factors affecting dust source areas including soil, lithology, slope, Normalized difference vegetation index (NDVI), geomorphology and land use were prepared. The performance of two models (WOE and FR) was evaluated using the area under curve (AUC) of the receiver operating characteristic curve. The results showed that soil, geomorphology and slope exhibited the greatest influence in the dust source areas. The 55.3% (according to FR) and 62.6% (according to WOE) of the total area were classified as high and very high potential dust sources, while both models displayed acceptable accuracy with subsurface levels of 0.704 for FR and 0.751 for WOE, although they predict different fractions of dust potential classes. Based on Shapley additive explanations (SHAP), three factors, i.e., soil, slope and NDVI have the highest impact on the model's output. Overall, combination of statistic-based predictive models (or data mining models), RS and game theory techniques can provide accurate maps of dust source areas in arid and semi-arid regions, which can be helpful for mitigation of negative effects of dust storms.
Mahdi BOROUGHANI, Sima POURHASHEMI, Hamid GHOLAMI, Dimitris G KASKAOUTIS. Predicting of dust storm source by combining remote sensing, statistic-based predictive models and game theory in the Sistan watershed, southwestern Asia. Journal of Arid Land, 2021, 13(11): 1103-1121.
Fig. 1Location (a) and elevation (b) maps of the Sistan watershed in Iran and Afghanistan
Fig. 2Flow chart of the dust source mapping procedure in the Sistan watershed and model evaluation. NDVI, normalized difference vegetation index; FR, frequency ratio; WOE, weights of evidence; AUC, area under curve. The abbreviations are the same as the following figures.
Fig. 3An example of dust storm detection in the Sistan watershed from MODIS satellite imagery with FCC (false color combination)
Fig. 4Locations (a) and photos (b-e) of field visits in the Iranian territory of the Sistan watershed from 12 to 16 July 2019. The figures b-e are the 1-4 observation sites in Figure 4a.
Fig. 5Inventory map with distribution of dust sources divided into training points (70%, red) and validation points (30%, green)
Fig. 6Dust source potential maps (DSPM) produced by FR (a) and WOE (b) models in the Sistan watershed
Dust source (%)
Area covered (%)
Potential class of dust source
Model
0.00
24.4
Low
FR
6.35
20.3
Moderate
50.79
40.8
High
42.86
14.5
Very high
0.00
12.8
Low
WOE
4.76
24.6
Moderate
15.87
27.1
High
79.37
35.5
Very high
Table 1 Percentage of dust source areas for each potential class and percentage of dust sources in the validation phase for FR and WOE models
Fig. 7AUC of the receiver operating characteristic (ROC) curve. (a) success rate curve for the dust source potential map of FR and WOE models (training phase); (b) forecast rate curve for the dust source potential map of FR and WOE models (validation phase).
Fig. 8Spatial maps of effective factors on the dust sources. (a) geomorphology; (b) land use; (c), lithology; (d) soil; (e) NDVI; (f) slope. The points show the dust source areas (training and validation points).
Factor
Number of pixels in class
Class (%)
Number of dust source
Dust source (%)
FR
WOE
Land use
Moderate rangeland
32,868.54
21.96
6
2.84
0.13
-0.98
Agriculture
5912.27
3.95
12
5.69
1.44
-0.98
Bare land
101,275.60
67.67
172
81.52
1.20
0.21
Marsh land
1266.97
0.85
0
0.00
0.00
0.00
Sand dune
3960.49
2.65
5
2.37
0.90
0.00
Water-dry lake
3897.69
2.60
16
7.58
2.91
0.21
Residental area
113.33
0.08
0
0.00
0.00
0.00
Flood plain
94.46
0.06
0
0.00
0.00
0.00
Salt land
278.41
0.19
0
0.00
0.00
0.00
Lithology
Sedimentary rocks-consolidated
96,815.97
64.69
111
52.61
0.81
0.54
Sedimentary rocks-unconsolidated
42,117.53
28.14
98
46.45
1.65
0.54
Volcano rocks
8087.59
5.40
2
0.95
0.18
-0.32
Intrusive rocks
1452.63
0.97
0
0.00
0.00
0.00
Metamorphic rocks
1194.05
0.80
0
0.00
0.00
0.00
Slope
0%-2%
30,529.54
20.40
107
50.71
2.49
0.43
2%-5%
23,344.82
15.60
43
20.38
1.31
-0.81
5%-8%
16,660.65
11.13
18
8.53
0.77
-0.81
8%-12%
16,630.44
11.11
19
9.00
0.81
-1.98
12%-32%
39,260.29
26.23
18
8.53
0.33
0.00
>32%
23,242.03
15.53
6
2.84
0.18
-1.98
Soil
Calcaric Fluvisols
914.13
0.61
0
0.00
0.00
-1.50
Calcic Yermosols
66,191.80
44.23
48
22.75
0.51
-0.57
Cambic Arenosols
6231.48
4.16
9
4.27
1.00
1.02
Lithosols
42,050.36
28.10
13
6.16
0.22
0.00
Orthic Solonchaks
31,610.87
21.12
129
61.14
2.88
0.91
Water bodies
1770.44
1.18
12
5.69
4.43
0.00
Geomorphology
Playa
21,037.10
14.06
84
39.81
2.83
1.02
Clay pan (Dagg)
29,216.91
19.52
63
29.86
1.53
0.44
Covered pediment
36,726.39
24.54
43
20.38
0.83
-0.32
Rock pediment
36,130.21
24.14
16
7.58
0.31
-0.83
Erosion pediment
23,359.55
15.61
5
2.37
0.15
-1.32
Mountain
3197.62
2.14
0
0.00
0.00
-1.32
NDVI
-0.4-0.0
148,415.93
99.16
211
100.00
1.01
0.01
0.0-0.1
1048.86
0.70
0
0.00
0.00
0.00
0.1-0.2
141.04
0.09
0
0.00
0.00
0.00
>0.2
61.94
0.04
0
0.00
0.00
0.00
Table 2 Relationship between each of the effective factors and dust source areas using FR and WOE models
WOE
Factor
FR
Factor
0.908
Soil
0.9080
Soil
0.693
Slope
0.6160
Geomorphology
0.616
Geomorphology
0.5750
Slope
0.508
NDVI
0.2000
Land use
0.200
Land use
0.1720
Lithology
0.172
Lithology
0.0001
NDVI
Table 3 Relative importance of each of effective factors on dust source areas according to FR and WOE models
Fig. 9(a) relative importance of factors controlling dust emissions by PFIM (permutation feature importance measure); (b) contribution of base value for factors controlling dust emissions by SHAP; (c) SHAP value for each factor.
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