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Mapping of dust source susceptibility by remote sensing and machine learning techniques (case study: Iran-Iraq border)

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Abstract

A dust storm is a major environmental problem affecting many arid regions worldwide. The novel contribution of this study is combining indicators extracted from RS- and statistic-based predictive models to spatial mapping of land susceptibility to dust emissions in a very important dust source area in the borders of Iran and Iraq (Khuzestan province in Iran and Al-Basrah and Maysan provinces in Iraq). In this research, remote sensing (RS) techniques and machine learning techniques, including multivariate adaptive regression spline (MARS), random forest (RF), and logistic regression (LR), were used for dust source identification and susceptibility map preparation. To this end, 152 DSA for the period of 2005–2020 were identified in the study area. Of these DSA data, 70% was assigned to the Dust Source Susceptibility Mapping (DSSM) (training dataset) and 30% to model validation. Consequently, six factors (i.e., soil, lithology, slope, normalized vegetation differential index (NDVI), geomorphology, and land use units) were prepared as DSA’s independent and effective variables. The results of all three models indicated that land use had the most impact on DSA. The validation results of these models using the test data showed sub-curves of 0.92, 0.86, and 0.76 for the RF, MARS, and LR models, respectively. Also, results showed that the RF model outperformed MARS (AUC = 0.89) and LR (AUC = 0.78) methods. In all three models, high and very high susceptibility classes generally covered a large percentage of the case study. The highest percentage of dust source points was also in this susceptibility category. Overall, the results of this study can be useful for planners and managers to control and reduce the risk of negative dust consequences.

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Data availability

The data that support the findings of this study are available from the corresponding author, Mohammad Ali Zangane Asadi, upon reasonable request.

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Funding

This work was supported by Mohammad Ali Zangane Asadi (Grant) from Hakim Sabzevari University.

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Authors and Affiliations

  1. Department of Geography, Hakim Sabzevari University, Sabzevar, Iran

    Sima Pourhashemi & Mohammad Ali Zangane Asadi

  2. Research Center for Geosciences and Social Studies, Hakim Sabzevari University, Sabzevar, Iran

    Mahdi Boroughani

  3. Department of Geography, Ghent University, Ghent, Belgium

    Hossein Azadi

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  1. Sima Pourhashemi
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  3. Mahdi Boroughani
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  4. Hossein Azadi
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Sima Pourhashemi, Mohammad Ali Zangane Asadi and Mahdi Boroughani. The first draft of the manuscript was written by Sima Pourhashemi and Mahdi Boroughani and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Mohammad Ali Zangane Asadi.

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Pourhashemi, S., Asadi, M.A.Z., Boroughani, M. et al. Mapping of dust source susceptibility by remote sensing and machine learning techniques (case study: Iran-Iraq border). Environ Sci Pollut Res 30, 27965–27979 (2023). https://doi.org/10.1007/s11356-022-23982-x

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