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Journal of Arid Land  2021, Vol. 13 Issue (11): 1103-1121    DOI: 10.1007/s40333-021-0023-3
Research article     
Predicting of dust storm source by combining remote sensing, statistic-based predictive models and game theory in the Sistan watershed, southwestern Asia
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
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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.

Key wordspotential dust source      remote sensing      frequency ratio      weight of evidence      dust emission     
Received: 12 August 2021      Published: 10 November 2021
Corresponding Authors: Mahdi BOROUGHANI     E-mail:
Cite this article:

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.

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Fig. 1 Location (a) and elevation (b) maps of the Sistan watershed in Iran and Afghanistan
Fig. 2 Flow 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. 3 An example of dust storm detection in the Sistan watershed from MODIS satellite imagery with FCC (false color combination)
Fig. 4 Locations (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. 5 Inventory map with distribution of dust sources divided into training points (70%, red) and validation points (30%, green)
Fig. 6 Dust 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. 7 AUC 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. 8 Spatial 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
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
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
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
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
-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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