Research Management System | University of Jiroft | Alireza Nafarzadegan

Name	Alireza Nafarzadegan
Affiliation	Assistant professor
Degree	—
Website	—
Email	—

	Title	Journal
1	Deciphering environmental determinants of soil metals in dust-influenced arid soils through interpretable models across data-availability scenarios	Journal of Environmental Management 404:129472 (2026) 1-18
2	Machine Learning approaches for identifying factors influencing dust sensitivity in the Gavkhouni Basin, Central Iran	International Journal of Environmental Science and Technology https://doi.org/10.1007/s13762-024-06183-6 (2024) 1-20
3	Modeling PM2.5 and SO2 levels and identifyingfactors impacting them in Kerman city,southeastern Iran	Modeling Earth Systems and Environment https://doi.org/10.1007/s40808-024-02103-2 (2024) https://doi.org/10.1
4	Determining Effective Factors Regarding Weather and Some Types of Air Pollutants in Seasonal Changes of PM10 Concentration Using Tree-Based Algorithms in Yazd City	Journal of Environmental Health and Sustainable Development 9(1) (2024) 2180-94
5	Influential environmental drivers on air pollution in dust‑sensitive regions of Iran	Arabian Journal of Geosciences 15:1388 (2022) 1-14
6	Monitoring the water surface of wetlands in Iran and their relationship with air pollution in nearby cities	ENVIRONMENTAL MONITORING AND ASSESSMENT 194 (7) (2022) 1-20
7	Predicting the dust events frequency around a degraded ecosystem and determining the contribution of their controlling factors using gradient boosting‑based approaches and game theory	ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH https://doi.org/10.1007/s11356-021-17265-0 (2022) 1-19
8	Spatial characterization of dust emission prone arid regions using feature extraction and predictive algorithms	APPLIED GEOGRAPHY 133:102495 (2021) 1-13
9	Using multivariate adaptive regression splines and extremely randomized trees algorithms to predict dust events frequency around an international wetland and prioritize its drivers	ENVIRONMENTAL MONITORING AND ASSESSMENT 193 (2021) 1-21
10	Predicting the ground-level pollutants concentrations and identifying the influencing factors using machine learning, wavelet transformation, and remote sensing techniques	Atmospheric pollution research https://doi.org/10.1016/j.apr.2021.101064 (2021) 1-15
11	Predicting the number of dusty days around the desert wetlands in southeastern Iran using feature selection and machine learning techniques	ECOLOGICAL INDICATORS 107 (107499) (2021) 1-15
12	Accuracy, uncertainty, and interpretability assessments of ANFIS models to predict dust concentration in semi-arid regions	ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH https://doi.org/10.1007/s11356-020-10957-z (2020) 1-15