Using multivariate adaptive regression splines and extremely randomized trees algorithms to predict dust events frequency around an international wetland and prioritize its drivers

This study aimed to evaluate the performance of multivariate adaptive regression splines (MARS) and extremely randomized trees (ERT) models for predicting the internal and external dust events frequencies (DEF) across the northeastern and southwestern regions of the Gavkhouni International Wetland....

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Vydáno v:Environmental monitoring and assessment Ročník 193; číslo 7; s. 437
Hlavní autoři: Ebrahimi-Khusfi, Zohre, Nafarzadegan, Ali Reza, Khosroshahi, Mohammad
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 01.07.2021
Springer Nature B.V
Témata:
Aerosol optical depth
aerosols
Algorithms
Atmospheric particulates
Atmospheric Protection/Air Quality Control/Air Pollution
Dust
Dust storms
Earth and Environmental Science
Ecology
Ecotoxicology
Environment
Environmental Management
Environmental monitoring
Environmental science
Hazard mitigation
Identification
Iran
Model accuracy
Monitoring/Environmental Analysis
Multivariate analysis
Optical analysis
Optical thickness
Performance evaluation
prediction
Regions
Splines
Surface wind
Vapor pressure
Vapour pressure
Wetlands
Wind speed
Winds
Iran
Climatic factors
Dust pollution
Iran
Degraded wetland
Remote sensing
Machine learning
ISSN:0167-6369, 1573-2959, 1573-2959
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Shrnutí:This study aimed to evaluate the performance of multivariate adaptive regression splines (MARS) and extremely randomized trees (ERT) models for predicting the internal and external dust events frequencies (DEF) across the northeastern and southwestern regions of the Gavkhouni International Wetland. These models were also evaluated to model the internal DEF (IDEF) across the northwestern, southeastern, northern, and western regions around the wetland. Furthermore, the main factors controlling DEF and IDEF were identified based on the importance value (IV) of predictors in the best model. The results showed that the ERT model increased the prediction accuracies by an average of 40%, compared to the MARS model. According to the IV obtained from the ERT model, aerosol optical depth (IV = 0.28), wetland discharge (IV = 0.25), near-surface wind speed (IV = 0.08), and erosive winds frequency (IV = 0.07) were identified as the most important factors controlling DEF across the northeastern and southwestern regions of the wetland. However, the erosive wind speed was detected as the major factor affecting the IDEF in the northern (IV = 0.16), western (IV = 0.18), and southeastern (IV = 0.65) regions of study wetland. It was also found that vapor pressure with IV of 0.32 had the greatest effect on IDEF variations across the northwestern region of the wetland. Overall, the results demonstrate the effectiveness of the ERT model in modeling the factors affecting DEF and IDEF, and the results may be used to mitigate dust events hazards around the Gavkhouni Wetland, in central Iran.
Bibliografie:ObjectType-Article-1
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ISSN:0167-6369
1573-2959
1573-2959
DOI:10.1007/s10661-021-09198-5