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Dust source susceptibility mapping based on remote sensing and machine learning techniques

delete2022-12-01
delete10
PRE
AI
R
Reza Jafari *
M
Mohadeseh Amiri
F
Fatemeh Asgari
M
Mostafa Tarkesh
DOI：10.1016/j.ecoinf.2022.101872delete
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Abstract

Abstract

En 中文
Dust source susceptibility modeling and mapping is the first step in managing the impacts of dust on environ-mental systems and human health. In this study, satellite products and terrestrial data were used to detect dust sources in central Iran using remote sensing and machine learning techniques. After recording 890 sites as dust sources based on field surveys and determining 14 independent variables affecting wind erosion and dust sources, dust source distribution maps were prepared through GLM (Generalized Linear Model), CTA (Classifi-cation Tree Analysis), ANN (Artificial Neural Network), MARS (Multivariate Adaptive Regression Spline), RF (Random Forest), Maxent (Maximum Entropy), and ensemble algorithms. Specifically, 70% of dust source sites were used as training data and 30% were used for algorithm performance evaluation through different statistical methods such as partial ROC (Receiver Operator Characteristic), sensitivity, specificity, and TSS (True Skill Statistics). According to the results, following the ensemble model, RF had the highest and GLM had the lowest performance in dust source detection. According to the ensemble model, precipitation with a mean weight of 0.3 followed by evaporation, temperature, and soil moisture with mean weight of 0.173, 0.16, and 0.153, respec-tively, were the main driving forces in dust susceptibility mapping. This model classified 40.92% of the study area with low potential, 15.37% with medium potential, 25.77% with high potential, and 17.94% with very high potential. The research findings indicate that the integration of remote sensing and prediction algorithms can be used as a useful means for predicting the spatial distribution of dust sources in arid and semi-arid regions.
Keywords:
Dust sources
Remote sensing
Prediction algorithms
Partial ROC

Journal

Ecological Informatics cover
Ecological Informatics
IF:
7.3
Papers:
3.7K
Citations:
1.3W

Organization

No organization information available