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Evaluation of machine learning models for predicting the temporal variations of dust storm index in arid regions of Iran

This work aimed to evaluate the applicability of nine machine learning models and their average for predicting the seasonal dust storm index (DSI) during 2000–2018 in arid regions. The results showed that the averaging method outperformed the other individual ML models in predicting DSI changes in all seasons.

Long-term effects of outdoor air pollution on mortality and morbidity–prediction using nonlinear autoregressive and artificial neural networks models

Artificial neural network and nonlinear autoregressive models are very powerful methods for accurate prediction of respiratory mortality and mobility with at least three inputs. These findings strongly support the need for policymakers to set targets to reduce carbon monoxide and nitrogen monoxide concentrations in the environment.

Machine Learning for Predicting Dust Storm

This work aimed to evaluate the applicability of nine machine learning models and their average for predicting the seasonal dust storm index (DSI) during 2000–2018 in arid regions. The results showed that the averaging method outperformed the other individual ML models in predicting DSI changes in all seasons.

Fourier Transform Infrared Spectroscopy to Predict SOC

The present study introduced a novel approach for predicting SOC by using a combination of environmental variables with FTIR spectra using tree-based ML models and the hybrid of tree-based models with BA algorithm.

Predicting the Ground Level Pollutants

This study was conducted to evaluate the performance of the support vector regression model with and without applying wavelet transformation for predicting the air pollution in Isfahan metropolis, central Iran.It was found that the dominant agents affecting the temporal changes of study pollutants are soil moisture and meteorological drought.

Predicting the ground-level pollutants concentrations and identifying the influencing factors using machine learning, wavelet transformation, and remote sensing techniques

This study was conducted to evaluate the performance of the support vector regression model with and without applying wavelet transformation for predicting the air pollution in Isfahan metropolis, central Iran.It was found that the dominant agents affecting the temporal changes of study pollutants are soil moisture and meteorological drought.

Using environmental variables and Fourier Transform Infrared Spectroscopy to predict soil organic carbon

The present study introduced a novel approach for predicting SOC by using a combination of environmental variables with FTIR spectra using tree-based ML models and the hybrid of tree-based models with BA algorithm.

Dust Related Impacts of Mining Operations on Soil

This study found that the effect of the prevailing wind direction is an important factor in affecting the footprint of the mine. The results demonstrated that significant negative effects on soil and vegetation related to mining activities where more outspoken and reached further away along the leeward side of the mine.

Dust-related impacts of mining operations on rangeland vegetation and soil, a case study in Yazd province, Iran

This study found that the effect of the prevailing wind direction is an important factor in affecting the footprint of the mine. The results demonstrated that significant negative effects on soil and vegetation related to mining activities where more outspoken and reached further away along the leeward side of the mine.

Predicting weathering indices in soils using FTIR spectra and random forest models

The main goal of this study was to analyze the weathering conditions of the most representative soils of West Azerbaijan, Northern Iran, using chemical indices, and to demonstrate the suitability of FTIR spectra and RF to predict these weathering indices and to identify the major soil components related to soil weathering.