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Aliakbar Mohammadifar

Showing results (1-10 of 11) with videos related to

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Scientific Reports|November 12, 2022
Novel deep learning hybrid models (CNN-GRU and DLDL-RF) for the susceptibility classification of dust sources in the Middle East: a global sourceHamid Gholami, Aliakbar Mohammadifar
Scientific Reports|September 7, 2022
Assessment of the uncertainty and interpretability of deep learning models for mapping soil salinity using DeepQuantreg and game theoryAliakbar Mohammadifar, Hamid Gholami, Shahram Golzari
Environmental Science and Pollution Research International|November 12, 2022
Stacking- and voting-based ensemble deep learning models (SEDL and VEDL) and active learning (AL) for mapping land subsidenceAliakbar Mohammadifar, Hamid Gholami, Shahram Golzari
Journal of Environmental Management|August 18, 2023
Novel integrated modelling based on multiplicative long short-term memory (mLSTM) deep learning model and ensemble multi-criteria decision making (MCDM) models for mapping flood riskAliakbar Mohammadifar, Hamid Gholami, Shahram Golzari
Environmental Science and Pollution Research International|March 24, 2021
Spatial modelling of soil salinity: deep or shallow learning models?Aliakbar Mohammadifar, Hamid Gholami, Shahram Golzari, et al.
The Science of the Total Environment|September 11, 2023
Interpretability of simple RNN and GRU deep learning models used to map land susceptibility to gully erosionHamid Gholami, Aliakbar Mohammadifar, Shahram Golzari, et al.
Scientific Reports|November 25, 2020
Mapping wind erosion hazard with regression-based machine learning algorithmsHamid Gholami, Aliakbar Mohammadifar, Dieu Tien Bui, et al.
Environmental Science and Pollution Research International|July 24, 2020
A new integrated data mining model to map spatial variation in the susceptibility of land to act as a source of aeolian dustHamid Gholami, Aliakbar Mohammadifar, Hamid Reza Pourghasemi, et al.
Environmental Pollution (Barking, Essex : 1987)|December 7, 2023
Intrinsic and extrinsic techniques for quantification uncertainty of an interpretable GRU deep learning model used to predict atmospheric total suspended particulates (TSP) in Zabol, Iran during the dusty period of 120-days windHamid Gholami, Aliakbar Mohammadifar, Reza Dahmardeh Behrooz, et al.
Scientific Reports|August 15, 2024
An assessment of global land susceptibility to wind erosion based on deep-active learning modelling and interpretation techniquesHamid Gholami, Aliakbar Mohammadifar, Yougui Song, et al.
Pageof 2

Showing results (1-10 of 11) with videos related to

Sort By:
Pageof 2
Scientific Reports|November 12, 2022
Novel deep learning hybrid models (CNN-GRU and DLDL-RF) for the susceptibility classification of dust sources in the Middle East: a global sourceHamid Gholami, Aliakbar Mohammadifar
Scientific Reports|September 7, 2022
Assessment of the uncertainty and interpretability of deep learning models for mapping soil salinity using DeepQuantreg and game theoryAliakbar Mohammadifar, Hamid Gholami, Shahram Golzari
Environmental Science and Pollution Research International|November 12, 2022
Stacking- and voting-based ensemble deep learning models (SEDL and VEDL) and active learning (AL) for mapping land subsidenceAliakbar Mohammadifar, Hamid Gholami, Shahram Golzari
Journal of Environmental Management|August 18, 2023
Novel integrated modelling based on multiplicative long short-term memory (mLSTM) deep learning model and ensemble multi-criteria decision making (MCDM) models for mapping flood riskAliakbar Mohammadifar, Hamid Gholami, Shahram Golzari
Environmental Science and Pollution Research International|March 24, 2021
Spatial modelling of soil salinity: deep or shallow learning models?Aliakbar Mohammadifar, Hamid Gholami, Shahram Golzari, et al.
The Science of the Total Environment|September 11, 2023
Interpretability of simple RNN and GRU deep learning models used to map land susceptibility to gully erosionHamid Gholami, Aliakbar Mohammadifar, Shahram Golzari, et al.
Scientific Reports|November 25, 2020
Mapping wind erosion hazard with regression-based machine learning algorithmsHamid Gholami, Aliakbar Mohammadifar, Dieu Tien Bui, et al.
Environmental Science and Pollution Research International|July 24, 2020
A new integrated data mining model to map spatial variation in the susceptibility of land to act as a source of aeolian dustHamid Gholami, Aliakbar Mohammadifar, Hamid Reza Pourghasemi, et al.
Environmental Pollution (Barking, Essex : 1987)|December 7, 2023
Intrinsic and extrinsic techniques for quantification uncertainty of an interpretable GRU deep learning model used to predict atmospheric total suspended particulates (TSP) in Zabol, Iran during the dusty period of 120-days windHamid Gholami, Aliakbar Mohammadifar, Reza Dahmardeh Behrooz, et al.
Scientific Reports|August 15, 2024
An assessment of global land susceptibility to wind erosion based on deep-active learning modelling and interpretation techniquesHamid Gholami, Aliakbar Mohammadifar, Yougui Song, et al.
Pageof 2