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[HTML][HTML] 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 …
in the arid and semi-arid regions of the world. Accurate prediction of the number of dusty …
A review on the hybridization of fuzzy systems and machine learning techniques
Fuzzy systems are used in modeling and implementation of many real-world applications
operating under an imprecise and uncertain environment. Such systems have effective …
operating under an imprecise and uncertain environment. Such systems have effective …
Review on identification and forecasting of dusty weather
Dusty weather is an extreme weather phenomenon that occurs frequently in the northern
China. It leads to a turbidity of the air and a sharp decline in visibility, causing adverse …
China. It leads to a turbidity of the air and a sharp decline in visibility, causing adverse …
[HTML][HTML] Determining the contribution of environmental factors in controlling dust pollution during cold and warm months of western Iran using different data mining …
Dust pollution is one of the major environmental crises in the arid regions of Iran and there is
a need to predict dust pollution and identify its controlling factors to help reduce its adverse …
a need to predict dust pollution and identify its controlling factors to help reduce its adverse …
Machine learning applications to dust storms: a meta-analysis
Dust storms are natural hazards that affect both people and properties. Therefore, it is
important to mitigate their risks by implementing an early notification system. Different …
important to mitigate their risks by implementing an early notification system. Different …
[HTML][HTML] Beyond prediction: An integrated post-hoc approach to interpret complex model in hydrometeorology
With the increasing application of machine learning (ML) in hydrometeorology, we face the
urge to demystify the ML's black-box nature because it usually does not provide physically …
urge to demystify the ML's black-box nature because it usually does not provide physically …
Predicting the ground-level pollutants concentrations and identifying the influencing factors using machine learning, wavelet transformation, and remote sensing …
This study was conducted to evaluate the performance of the support vector regression
(SVR) model with and without applying wavelet transformation for predicting the PM10, PM2 …
(SVR) model with and without applying wavelet transformation for predicting the PM10, PM2 …
Aridity index variations and dust events in Iran from 1990 to 2018
This study was carried out to determine whether the changes in dust concentration (DC) in
Iran were attributed to changes in aridity index (AI) from 1990 to 2018. Long-term …
Iran were attributed to changes in aridity index (AI) from 1990 to 2018. Long-term …
Evaluation of the climate change effects on the future drought characteristics of Iranian wetlands
In recent years, climate change has widely affected different ecosystem conditions,
especially natural wetlands across different regions of the world. This study was aimed at …
especially natural wetlands across different regions of the world. This study was aimed at …
Optimization of neural network parameters in improvement of particulate matter concentration prediction of open-pit mining
The prediction of particulate matter (PM) concentration around open-pit mining is crucial for
its control. To achieve this, machine learning (ML) techniques have been attempted in PM …
its control. To achieve this, machine learning (ML) techniques have been attempted in PM …