| Spatial mapping of the provenance of storm dust: Application of data mining and ensemble modelling H Gholami, A Mohamadifar, AL Collins Atmospheric Research 233, 104716, 2020 | 107 | 2020 |
| Machine-learning algorithms for predicting land susceptibility to dust emissions: The case of the Jazmurian Basin, Iran H Gholami, A Mohamadifar, A Sorooshian, JD Jansen Atmospheric pollution research 11 (8), 1303-1315, 2020 | 97 | 2020 |
| Mapping wind erosion hazard with regression-based machine learning algorithms H Gholami, A Mohammadifar, DT Bui, AL Collins Scientific Reports 10 (1), 20494, 2020 | 63 | 2020 |
| Assessment of the interpretability of data mining for the spatial modelling of water erosion using game theory A Mohammadifar, H Gholami, JR Comino, AL Collins Catena 200, 105178, 2021 | 62 | 2021 |
| Using the Boruta algorithm and deep learning models for mapping land susceptibility to atmospheric dust emissions in Iran H Gholami, A Mohammadifar, S Golzari, DG Kaskaoutis, AL Collins Aeolian Research 50, 100682, 2021 | 54 | 2021 |
| Integrated modelling for mapping spatial sources of dust in central Asia-An important dust source in the global atmospheric system H Gholami, A Mohammadifar, H Malakooti, Y Esmaeilpour, S Golzari, ... Atmospheric Pollution Research 12 (9), 101173, 2021 | 42 | 2021 |
| Interpretability of simple RNN and GRU deep learning models used to map land susceptibility to gully erosion H Gholami, A Mohammadifar, S Golzari, Y Song, B Pradhan Science of the Total Environment 904, 166960, 2023 | 40 | 2023 |
| A new integrated data mining model to map spatial variation in the susceptibility of land to act as a source of aeolian dust H Gholami, A Mohammadifar, HR Pourghasemi, AL Collins Environmental Science and Pollution Research 27, 42022-42039, 2020 | 32 | 2020 |
| Novel integrated modelling based on multiplicative long short-term memory (mLSTM) deep learning model and ensemble multi-criteria decision making (MCDM) models for mapping … A Mohammadifar, H Gholami, S Golzari Journal of Environmental Management 345, 118838, 2023 | 31 | 2023 |
| Novel deep learning hybrid models (CNN-GRU and DLDL-RF) for the susceptibility classification of dust sources in the Middle East: a global source H Gholami, A Mohammadifar Scientific Reports 12 (1), 19342, 2022 | 30 | 2022 |
| Spatial modelling of soil salinity: deep or shallow learning models? A Mohammadifar, H Gholami, S Golzari, AL Collins Environmental Science and Pollution Research 28, 39432-39450, 2021 | 30 | 2021 |
| Mapping of the wind erodible fraction of soil by bidirectional gated recurrent unit (BiGRU) and bidirectional recurrent neural network (BiRNN) deep learning models M Rezaei, A Mohammadifar, H Gholami, M Mina, MJPM Riksen, ... Catena 223, 106953, 2023 | 28 | 2023 |
| Predicting land susceptibility to atmospheric dust emissions in central Iran by combining integrated data mining and a regional climate model H Gholami, A Mohamadifar, S Rahimi, DG Kaskaoutis, AL Collins Atmospheric Pollution Research 12 (4), 172-187, 2021 | 28 | 2021 |
| Stacking-and voting-based ensemble deep learning models (SEDL and VEDL) and active learning (AL) for mapping land subsidence A Mohammadifar, H Gholami, S Golzari Environmental Science and Pollution Research 30 (10), 26580-26595, 2023 | 23 | 2023 |
| Assessment of the uncertainty and interpretability of deep learning models for mapping soil salinity using DeepQuantreg and game theory A Mohammadifar, H Gholami, S Golzari Scientific Reports 12 (1), 15167, 2022 | 21 | 2022 |
| Modeling land susceptibility to wind erosion hazards using LASSO regression and graph convolutional networks H Gholami, A Mohammadifar, KE Fitzsimmons, Y Li, DG Kaskaoutis Frontiers in Environmental Science 11, 1187658, 2023 | 18 | 2023 |
| Intrinsic and extrinsic techniques for quantification uncertainty of an interpretable GRU deep learning model used to predict atmospheric total suspended particulates (TSP) in … H Gholami, A Mohammadifar, RD Behrooz, DG Kaskaoutis, Y Li, Y Song Environmental Pollution 342, 123082, 2024 | 13 | 2024 |
| Combination of Multi-criteria Decision-making Models and Regional Flood Analysis Technique to Prioritize Subwatersheds for Flood Control (Case study: Dehbar Watershed of Khorasan) AM Ali Reza Nafarzadegan Geography and Environmental Hazards 30, 2019 | 11* | 2019 |
| An assessment of global land susceptibility to wind erosion based on deep-active learning modelling and interpretation techniques H Gholami, A Mohammadifar, Y Song, Y Li, P Rahmani, DG Kaskaoutis, ... Scientific Reports 14 (1), 18951, 2024 | 7 | 2024 |
| Mapping wind erosion hazard with regression-based machine learning algorithms. Sci Rep 10: 20494 H Gholami, A Mohammadifar, DT Bui, AL Collins | 5 | 2020 |
Hamid GholamiProfessor at University of Hormozganبريد إلكتروني تم التحقق منه على hormozgan.ac.ir