Evaluation of machine learning models for predicting the temporal variations of dust storm index in arid regions of Iran
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- @Article{EBRAHIMIKHUSFI:2020:APR,
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author = "Zohre Ebrahimi-Khusfi and
Ruhollah Taghizadeh-Mehrjardi and Maryam Mirakbari",
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title = "Evaluation of machine learning models for predicting
the temporal variations of dust storm index in arid
regions of Iran",
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journal = "Atmospheric Pollution Research",
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year = "2020",
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ISSN = "1309-1042",
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DOI = "doi:10.1016/j.apr.2020.08.029",
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URL = "http://www.sciencedirect.com/science/article/pii/S1309104220302579",
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keywords = "genetic algorithms, genetic programming, Machine
learning, Remote sensing data, Climatic parameters,
Dust emissions, Dry lands, Iran",
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abstract = "It is necessary to predict wind erosion events and
specify the related effective factors to prioritize
management and executive measures to combat
desertification caused by wind erosion in arid areas.
Therefore, this work aimed to evaluate the
applicability of nine machine learning (ML) models
(including multivariate adaptive regression splines,
least absolute shrinkage and selection operator,
k-nearest neighbors, genetic programming, support
vector machine, Cubist, artificial neural networks,
extreme gradient boosting, random forest) and their
average for predicting the seasonal dust storm index
(DSI) during 2000-2018 in arid regions of Iran. The
results showed that the averaging method outperformed
the other individual ML models in predicting DSI
changes in all seasons. For instance, the averaging
methods improved the prediction accuracies for winter,
spring, summer, autumn, and dusty seasons by 22percent,
39percent, 28percent, 32percent, and 26percent,
respectively, compared to the multivariate adaptive
regression splines. Furthermore, the most important
factors in predicting DSI were detected as follows:
wind speed for winter, enhanced vegetation index for
spring, maximum wind speed for summer, autumn and dusty
seasons. In general, our results indicate that the
combining of the individual ML models by averaging
method help us to develop a more accurate approach for
predicting the temporal changes of the dust events in
arid regions. Furthermore, the obtained results in this
study can be applicable for prioritizing measures in
order to minimize the dangers of wind erosion based on
the major driving factors",
- }
Genetic Programming entries for
Zohre Ebrahimi-Khusfi
Ruhollah Taghizadeh-Mehrjardi
Maryam Mirakbari
Citations