Predicting the number of dusty days around the desert wetlands in southeastern Iran using feature selection and machine learning techniques

In the past decades, some desert wetlands have become critical regions for dust production in the arid and semi-arid regions of the world. Accurate prediction of the number of dusty days (NDDs) in these areas is of great importance. The most popular method for predicting climatic and environmental v...

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Bibliographic Details
Main Authors:	Dargahian, F. (Author), Ebrahimi-Khusfi, Z. (Author), Nafarzadegan, A.R (Author)
Format:	Article
Language:	English
Published:	Elsevier B.V. 2021
Subjects:	Arid and semi-arid regions Boruta decision making Decision making Decision trees Determination coefficients Dust Dust emissions Environmental variables Feature extraction Forecasting Hamoun wetlands Inference engines Iran Learning systems machine learning Machine learning techniques MARS Mean square error Multicollinearity Multivariate adaptive regression splines prediction Random processes Recursive feature elimination Root mean square errors semiarid region Stochastic gradient boosting Stochastic models Stochastic systems Support vector machines Support vector regression temporal analysis wetland Wetlands
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Predicting the number of dusty days around the desert wetlands in southeastern Iran using feature selection and machine learning techniques

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