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Omid Rahmati

Showing results (1-10 of 20) with videos related to

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The Science of the Total Environment|July 1, 2016
Application of Dempster-Shafer theory, spatial analysis and remote sensing for groundwater potentiality and nitrate pollution analysis in the semi-arid region of Khuzestan, IranOmid Rahmati, Assefa M Melesse
The Science of the Total Environment|October 6, 2017
River suspended sediment modelling using the CART model: A comparative study of machine learning techniquesBahram Choubin, Hamid Darabi, Omid Rahmati, et al.
Journal of Environmental Management|July 30, 2023
Scrutinization of land subsidence rate using a supportive predictive model: Incorporating radar interferometry and ensemble soft-computingBahram Choubin, Kourosh Shirani, Farzaneh Sajedi Hosseini, et al.
The Science of the Total Environment|November 27, 2016
Evaluating the influence of geo-environmental factors on gully erosion in a semi-arid region of Iran: An integrated frameworkOmid Rahmati, Naser Tahmasebipour, Ali Haghizadeh, et al.
The Science of the Total Environment|April 11, 2023
Mapping of salty aeolian dust-source potential areas: Ensemble model or benchmark models?Bahram Choubin, Farzaneh Sajedi Hosseini, Omid Rahmati, et al.
Journal of Environmental Management|February 17, 2019
Land subsidence hazard modeling: Machine learning to identify predictors and the role of human activitiesOmid Rahmati, Ali Golkarian, Trent Biggs, et al.
The Science of the Total Environment|September 22, 2018
How can statistical and artificial intelligence approaches predict piping erosion susceptibility?Mohsen Hosseinalizadeh, Narges Kariminejad, Omid Rahmati, et al.
The Science of the Total Environment|February 13, 2019
A novel machine learning-based approach for the risk assessment of nitrate groundwater contaminationFarzaneh Sajedi-Hosseini, Arash Malekian, Bahram Choubin, et al.
The Science of the Total Environment|November 27, 2018
Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy modelsAli Azareh, Omid Rahmati, Elham Rafiei-Sardooi, et al.
The Science of the Total Environment|December 3, 2019
Fog-water harvesting Capability Index (FCI) mapping for a semi-humid catchment based on socio-environmental variables and using artificial intelligence algorithmsZahra Karimidastenaei, Ali Torabi Haghighi, Omid Rahmati, et al.
Pageof 2

Showing results (1-10 of 20) with videos related to

Sort By:
Pageof 2
The Science of the Total Environment|July 1, 2016
Application of Dempster-Shafer theory, spatial analysis and remote sensing for groundwater potentiality and nitrate pollution analysis in the semi-arid region of Khuzestan, IranOmid Rahmati, Assefa M Melesse
The Science of the Total Environment|October 6, 2017
River suspended sediment modelling using the CART model: A comparative study of machine learning techniquesBahram Choubin, Hamid Darabi, Omid Rahmati, et al.
Journal of Environmental Management|July 30, 2023
Scrutinization of land subsidence rate using a supportive predictive model: Incorporating radar interferometry and ensemble soft-computingBahram Choubin, Kourosh Shirani, Farzaneh Sajedi Hosseini, et al.
The Science of the Total Environment|November 27, 2016
Evaluating the influence of geo-environmental factors on gully erosion in a semi-arid region of Iran: An integrated frameworkOmid Rahmati, Naser Tahmasebipour, Ali Haghizadeh, et al.
The Science of the Total Environment|April 11, 2023
Mapping of salty aeolian dust-source potential areas: Ensemble model or benchmark models?Bahram Choubin, Farzaneh Sajedi Hosseini, Omid Rahmati, et al.
Journal of Environmental Management|February 17, 2019
Land subsidence hazard modeling: Machine learning to identify predictors and the role of human activitiesOmid Rahmati, Ali Golkarian, Trent Biggs, et al.
The Science of the Total Environment|September 22, 2018
How can statistical and artificial intelligence approaches predict piping erosion susceptibility?Mohsen Hosseinalizadeh, Narges Kariminejad, Omid Rahmati, et al.
The Science of the Total Environment|February 13, 2019
A novel machine learning-based approach for the risk assessment of nitrate groundwater contaminationFarzaneh Sajedi-Hosseini, Arash Malekian, Bahram Choubin, et al.
The Science of the Total Environment|November 27, 2018
Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy modelsAli Azareh, Omid Rahmati, Elham Rafiei-Sardooi, et al.
The Science of the Total Environment|December 3, 2019
Fog-water harvesting Capability Index (FCI) mapping for a semi-humid catchment based on socio-environmental variables and using artificial intelligence algorithmsZahra Karimidastenaei, Ali Torabi Haghighi, Omid Rahmati, et al.
Pageof 2