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...
Saved in:
Main Authors: | Zohre Ebrahimi-Khusfi, Ali Reza Nafarzadegan, Fatemeh Dargahian |
---|---|
Format: | article |
Language: | EN |
Published: |
Elsevier
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/39ed9e7b457048f5801bb7c97af014ab |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Machine Learning and Deterministic Approach to the Reflective Ultrasound Tomography
by: Dariusz Majerek, et al.
Published: (2021) -
Experimental Analysis of GBM to Expand the Time Horizon of Irish Electricity Price Forecasts
by: Conor Lynch, et al.
Published: (2021) -
Ridge Estimation's Effectiveness for Multiple Linear Regression with Multicollinearity: An Investigation Using Monte-Carlo Simulations
by: O. G. Obadina, et al.
Published: (2021) -
Mapping Population Distribution Based on XGBoost Using Multisource Data
by: Xin Zhao, et al.
Published: (2021) -
Determining the contribution of environmental factors in controlling dust pollution during cold and warm months of western Iran using different data mining algorithms and game theory
by: Zohre Ebrahimi-Khusfi, et al.
Published: (2021)