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Integrated quantitative and qualitative GIS based drought analysis and salt/dust storm impacts on food security | ||
| Earth Observation and Geomatics Engineering | ||
| دوره 9، شماره 1، شهریور 2025 | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22059/eoge.2026.409715.1196 | ||
| نویسندگان | ||
| Peyman Yariyan1؛ Bakhtiar Feizizadeh* 2؛ Thomas Blaschke3؛ Sadra Karimzadeh2؛ Murat Yakar4 | ||
| 1Department of Remote sensing and GIS, University of Tabriz, Tabriz, Iran | ||
| 2Department of Remote Sensing and GIS, University of Tabriz, Tabriz, Iran | ||
| 3Department of Applied Geoinformatics Z_GIS, University of Salzburg, Austria | ||
| 4Department of Geoinformatics, University of Mersin, Mersin, Turkey | ||
| چکیده | ||
| Dust storms are a growing environmental hazard in the drought-affected Urmia Lake Basin, Iran, threatening agricultural productivity and public health. This study employs four advanced machine learning algorithms—Support Vector Machine (SVM), Gradient Boosting Decision Trees (GBDT), Random Forest (RF), and Logistic Regression (LR)—to model dust storm susceptibility using a comprehensive set of environmental, climatic, and soil variables. The models were trained and validated on remote sensing and ground data, achieving strong performance metrics, with SVM attaining the highest Area under the Curve (AUC) of 0.94, indicating excellent discriminative ability. Susceptibility maps produced by these models reveal significant spatial variability in vulnerability, highlighting extensive agricultural lands and populated areas at risk. The increasing frequency of dust storms due to Lake Urmia’s desiccation directly threatens food security by reducing crop yields and exacerbates respiratory and cardiovascular health issues among local populations. These findings emphasize the urgent need for targeted environmental management and public health strategies to mitigate dust storm impacts and enhance resilience in this ecologically sensitive region. | ||
| کلیدواژهها | ||
| Dust Storm؛ Machine Learning؛ Food Security؛ GIS؛ Susceptibility Mapping | ||
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آمار تعداد مشاهده مقاله: 601 |
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