Matches in SemOpenAlex for { <https://semopenalex.org/work/W3028207908> ?p ?o ?g. }
- W3028207908 endingPage "139508" @default.
- W3028207908 startingPage "139508" @default.
- W3028207908 abstract "Dust particles are transported globally. Dust storms can adversely impact both human health and the environment, but they also impact transportation infrastructure, agriculture, and industry, occasionally severely. The identification of the locations that are the primary sources of dust, especially in arid and semi-arid environments, remains a challenge as these sites are often in remote or data-scarce regions. In this study, a new method using state-of-the-art machine-learning algorithms – random forest (RF), support vector machines (SVM), and multivariate adaptive regression splines (MARS) – was evaluated for its ability to spatially model the distribution of dust-source potential in eastern Iran. To accomplish this, empirically identified dust-source locations were determined with the ozone monitoring instrument aerosol index and the Moderate-Resolution Imaging Spectroradiometer (MODIS) Deep Blue aerosol optical thickness methods. The identified areas were divided into training (70%) and validation (30%) sets. Measurements of the conditioning factors (lithology, wind speed, maximum air temperature, land use, slope angle, soil, rainfall, and land cover) were compiled for the study area and predictive models were developed. The area-under-the-receiver operating characteristics curve (AUC) and true-skill statistics (TSS) were used to validate the maps of the models' predictions. The results show that the RF algorithm performed best (AUC = 89.4% and TSS = 0.751), followed by the SVM (AUC = 87.5%, TSS = 0.73) and the MARS algorithm (AUC = 81%, TSS = 0.69). The results of the RF indicated that wind speed and land cover are the most important factors affecting dust generation. The region of highest dust-source potential that was identified by the RF is in the eastern parts of the study region. This model can be applied to other arid and semi-arid environments that experience dust storms to promote management that prevents desertification and reduces dust production." @default.
- W3028207908 created "2020-05-29" @default.
- W3028207908 creator A5002491092 @default.
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- W3028207908 creator A5039567207 @default.
- W3028207908 creator A5041402625 @default.
- W3028207908 creator A5062128630 @default.
- W3028207908 creator A5063887607 @default.
- W3028207908 creator A5076352077 @default.
- W3028207908 date "2020-10-01" @default.
- W3028207908 modified "2023-10-15" @default.
- W3028207908 title "Identifying sources of dust aerosol using a new framework based on remote sensing and modelling" @default.
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