Defining a Method for Mapping Aeolian Sand Transport Susceptibility Using Bivariate Statistical and Machine Learning Methods—A Case Study of the Seqale Watershed, Eastern Iran

Mehdi Bashiri, Mohammad Reza Rahdari, Francisco Serrano-Bernardo, Jesús Rodrigo-Comino and Andrés Rodríguez-Seijo ()
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Mehdi Bashiri: Faculty of Agriculture, University of Torbat Heydarieh, Torbat Heydarieh 9516168595, Iran
Mohammad Reza Rahdari: Faculty of Agriculture, University of Torbat Heydarieh, Torbat Heydarieh 9516168595, Iran
Francisco Serrano-Bernardo: Departamento de Ingeniería Civil, ETSI Caminos, Canales y Puertos, Universidad de Granada, Campus Fuentenueva, s/n, 18071 Granada, Spain
Jesús Rodrigo-Comino: Department de Análisis Geográfico Regional y Geografía Física, Facultad de Filosofía y Letras, Universidad de Granada, 18071 Granada, Spain
Andrés Rodríguez-Seijo: Departamento de Bioloxía Vexetal e Ciencia do Solo, Facultade de Ciencias, Universidade de Vigo, As Lagoas s/n, 32004 Ourense, Spain

Sustainability, 2025, vol. 17, issue 18, 1-22

Abstract: Desert regions face unique challenges under climate change, including the emerging phenomenon of sand dune expansion. This research investigates aeolian sand transport in the Seqale watershed (eastern Iran) using geostatistical and machine learning methods to model and forecast dune spread, aiming to reduce the loss of sustainability in these valuable landscapes. Predictor variables (altitude, slope, climate, land use, etc.) and wind erosion occurrence were analyzed using classification algorithms (decision tree, random forest, etc.) and bivariate methods (information value, area density) in R software 4.5.0. Risk zoning maps were created and evaluated by combining these approaches. Results indicate a higher sand dune presence in regions with specific altitude (1200–1400 m), gentle northeast-facing slopes (2–5 degrees), moderate rainfall (250–500 mm), high evaporation (2500–3000 mm), outside flood plains, and far from roads (>3000 m) and water channels (>500 m). Dune expansion maps based on density area and information value methods showed substantial areas classified as high to very high movement risk. Machine learning analysis identified the Support Vector Machine (SVM) algorithm (AUC = 0.94) as the most effective for classifying sand dune zones. The study concludes that spatial forecasts, combined with tailored physical and biological measures, are essential for effective sand dune management in the region.

Keywords: aeolian geomorphology; arid lands; classification algorithm; climate resilience; desertification; ecosystem vulnerability; land degradation; surface processes; wind erosion (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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