Dust source susceptibility mapping based on remote sensing and machine learning techniques |
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| Affiliation: | 1. Department of Natural Resources, Isfahan University of Technology, Isfahan 84156983111, Iran;2. Department of Natural Resources, University of Applied Science and Technology (UAST), Branch of Mazandaran Wood and Paper Industries, Po Box: 4819174555, Sari, Iran;1. Department of Health Science and Biostatistics, School of Health Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia;2. Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia;3. Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria, Australia;1. Shaanxi Meteorological Service Center of Agricultural Remote Sensing and Economic Crops, Xi''an 710014, China;2. National Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System Science, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China;3. Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China;1. Biodiversity Centre, Finnish Environment Institute, Latokartanonkaari 11, FI-00790 Helsinki, Finland;2. Finnish Meteorological Institute, Weather and climate change impact research, P.O. Box 503, FI-00101 Helsinki, Finland;3. Department of Geosciences and Geography, University of Helsinki, P.O. Box 64, Gustaf Hällströminkatu 2a, 00014 Helsinki, Finland;1. Key Laboratory of Ministry of Education for Coastal Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Fujian 361102, China;2. Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Xiamen University, 361102, China;3. Xiamen Key Laboratory of Urban Sea Ecological Conservation and Restoration (USER), Xiamen University, 361102, China;4. Coastal and Ocean Management Institute, Xiamen University, 361102, China;5. School of Energy and Environmental Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing 100083, China;6. Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, Beijing 100083, China |
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| Abstract: | Dust source susceptibility modeling and mapping is the first step in managing the impacts of dust on environmental 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 (Classification 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, respectively, 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. |
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