Work

Evaluating key environmental variables in dust occurrence through artificial intelligence methods

Year: 2026

Type: article

Abstract: Dust storms in arid and semi-arid regions present a critical environmental challenge, arising from complex interactions between climatic patterns, landform features, and surface conditions. The Sistan and Baluchestan Province in southeastern Iran is a global dust hotspot, yet the quantitative impact of its diverse environmental drivers remains poorly understood. This study quantifies these relationships by evaluating the influence of 18 environmental variables on dust occurrence over a 20-year period (2003−2023). To achieve this, we developed a robust framework combining six machine learning algorithms (Logistic Regression, KNN, SVM, MLP, Random Forest, and XGBoost) with a rigorous hybrid feature selection strategy (VIF-RFE). While ensemble models (Random Forest and XGBoost) and KNN demonstrated superior performance (AUC ~0.89), capturing the non-linear nature of dust generation, the Shapley Additive exPlanations (SHAP) analysis revealed a clear hierarchy of drivers. Topographic features, specifically elevation, were the dominant broad-scale predictor, followed closely by hydro-climatic factors (precipitation and NDMI) and soil chemical properties (Salinity/VSSI). In alignment with the region's hyper-arid characteristics, vegetation indices (e.g., NDVI, MSAVI) showed minimal predictive power. As over 93% of the landscape constitutes naturally barren soil or sparse cover, the vegetation falls below the critical threshold required to act as a significant wind buffer, leaving abiotic drivers (soil moisture and salinity) as the primary determinants of dust generation. These findings highlight that mitigation strategies must prioritize soil moisture preservation and salinity stabilization over isolated revegetation efforts, providing a scientific foundation for targeted management in hyper-arid regions. • Applied artificial intelligence algorithms to evaluate the impact of environmental variables on dust occurrence. • Dust occurrence identified through Aerosol Optical Depth and synoptic station. • Based on Shapley Additive exPlanation methodology, climate factors enforced the greatest influence.

Source: Ecological Informatics

DOI: 10.1016/j.ecoinf.2026.103653

Authors: Mohammad Mahdi Pourhanifeh, Hassan Khosravi, Tayebeh Mesbah Zadeh, Esmaeil Heydari Alamdarloo, Azam Abolhasani

Institution: University of Tehran

Language: English

Topic: Aeolian processes and effects

Subfield: Earth-Surface Processes

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDGs: Climate action, Life on land

Open Access status: gold

Estimated APC paid: $2,770