| Assessing the performance of GIS-based machine learning models with different accuracy measures for determining susceptibility to gully erosion Y Garosi, M Sheklabadi, C Conoscenti, HR Pourghasemi, K Van Oost Science of the Total Environment 664, 1117-1132, 2019 | 184 | 2019 |
| Comparison of differences in resolution and sources of controlling factors for gully erosion susceptibility mapping Y Garosi, M Sheklabadi, HR Pourghasemi, AA Besalatpour, C Conoscenti, ... Geoderma 330, 65-78, 2018 | 157 | 2018 |
| Improving the spatial prediction of soil organic carbon using environmental covariates selection: A comparison of a group of environmental covariates M Zeraatpisheh, Y Garosi, HR Owliaie, S Ayoubi, R Taghizadeh-Mehrjardi, ... Catena 208 (105723), 2022 | 130 | 2022 |
| Predicting heavy metal contents by applying machine learning approaches and environmental covariates in west of Iran K Azizi, S Ayoubi, K Nabiollahi, Y Garosi, R Gislum Journal of Geochemical Exploration 233, 106921, 2022 | 81 | 2022 |
| Integration of Sentinel-1/2 and topographic attributes to predict the spatial distribution of soil texture fractions in some agricultural soils of western Iran K Azizi, Y Garosi, S Ayoubi, S Tajik Soil and Tillage Research 229, 105681, 2023 | 25 | 2023 |
| Effects of different sources and spatial resolutions of environmental covariates on predicting soil organic carbon using machine learning in a semi-arid region of Iran Y Garosi, S Ayoubi, M Nussbaum, M Sheklabadi Geoderma Regional, 2022 | 23 | 2022 |
| Use of the time series and multi-temporal features of Sentinel-1/2 satellite imagery to predict soil inorganic and organic carbon in a low-relief area with a semi-arid environment Y Garosi, S Ayoubi, M Nussbaum, M Sheklabadi, M Nael, I Kimiaee International Journal of Remote Sensing 43 (18), 6856–6880, 2022 | 15 | 2022 |
| Feasibility of using environmental covariates and machine learning to predict the spatial variability of selected heavy metals in soils M Zeraatpisheh, R Mirzaei, Y Garosi, M Xu, GBM Heuvelink, T Scholten, ... EGU General Assembly 2020, 2020 | 1 | 2020 |
| Corrigendum to" Assessing the performance of GIS-based machine learning models with different accuracy measures for determining susceptibility to gully erosion"[Sci. Total … Y Garosi, M Sheklabadi, C Conoscenti, HR Pourghasemi, K Van Oost The Science of the total environment 730, 139262, 2020 | 1 | 2020 |
| GIS-based multivariate predictive models for gully erosion susceptibility mapping in calcareous soils Y Garosi, M Sheklabadi, C Conocenti, KV Oost, P Shekaari, L Meimivand Pedometrics 2017, 2017 | | 2017 |
| Soil erosion status in Iran and clay minerals influence on soils interrill erodibility factor (A case study: Dasht - e- Tabriz) AA Jfatzadeh, Y Garosi, S Oustan, A Ahmadi Soil and Crop Management: Adaptation and Mitigation of Climate Change, 2013 | | 2013 |
Professor Shamsollah AyoubiProfessor of Soil Science, Isfahan University of TechnologyVerified email at cc.iut.ac.ir
