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Alireza Arabameri

Showing results (1-10 of 27) with videos related to

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Environmental Science and Pollution Research International|February 1, 2024
Land subsidence susceptibility mapping: a new approach to improve decision stump classification (DSC) performance and combine it with four machine learning algorithmsRui Zhao, Alireza Arabameri, M Santosh
Journal of Environmental Management|January 5, 2021
Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GISAlireza Arabameri, Biswajeet Pradhan, Khalil Rezaei
Scientific Reports|September 24, 2025
Machine learning model optimization for flood susceptibility zonation over the Kosi megafan, Himalayan foreland basin, IndiaAman Arora, Purna Durga G, Manish Pandey, et al.
Environmental Science and Pollution Research International|June 2, 2023
Land subsidence susceptibility mapping: comparative assessment of the efficacy of the five modelsLei Zhang, Alireza Arabameri, M Santosh, et al.
Environmental Science and Pollution Research International|February 3, 2023
Optimizing machine learning algorithms for spatial prediction of gully erosion susceptibility with four training scenariosGuoqing Liu, Alireza Arabameri, M Santosh, et al.
The Science of the Total Environment|January 15, 2019
A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern IranAlireza Arabameri, Khalil Rezaei, Artemi Cerdà, et al.
Sensors (Basel, Switzerland)|March 4, 2020
Machine Learning-Based Gully Erosion Susceptibility Mapping: A Case Study of Eastern IndiaSunil Saha, Jagabandhu Roy, Alireza Arabameri, et al.
The Science of the Total Environment|December 22, 2018
GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approachesAlireza Arabameri, Khalil Rezaei, Artemi Cerda, et al.
The Science of the Total Environment|July 1, 2019
Novel ensembles of COPRAS multi-criteria decision-making with logistic regression, boosted regression tree, and random forest for spatial prediction of gully erosion susceptibilityAlireza Arabameri, Mojtaba Yamani, Biswajeet Pradhan, et al.
The Science of the Total Environment|May 14, 2020
Predicting the deforestation probability using the binary logistic regression, random forest, ensemble rotational forest, REPTree: A case study at the Gumani River Basin, IndiaSunil Saha, Mantosh Saha, Kaustuv Mukherjee, et al.
Pageof 3

Showing results (1-10 of 27) with videos related to

Sort By:
Pageof 3
Environmental Science and Pollution Research International|February 1, 2024
Land subsidence susceptibility mapping: a new approach to improve decision stump classification (DSC) performance and combine it with four machine learning algorithmsRui Zhao, Alireza Arabameri, M Santosh
Journal of Environmental Management|January 5, 2021
Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GISAlireza Arabameri, Biswajeet Pradhan, Khalil Rezaei
Scientific Reports|September 24, 2025
Machine learning model optimization for flood susceptibility zonation over the Kosi megafan, Himalayan foreland basin, IndiaAman Arora, Purna Durga G, Manish Pandey, et al.
Environmental Science and Pollution Research International|June 2, 2023
Land subsidence susceptibility mapping: comparative assessment of the efficacy of the five modelsLei Zhang, Alireza Arabameri, M Santosh, et al.
Environmental Science and Pollution Research International|February 3, 2023
Optimizing machine learning algorithms for spatial prediction of gully erosion susceptibility with four training scenariosGuoqing Liu, Alireza Arabameri, M Santosh, et al.
The Science of the Total Environment|January 15, 2019
A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern IranAlireza Arabameri, Khalil Rezaei, Artemi Cerdà, et al.
Sensors (Basel, Switzerland)|March 4, 2020
Machine Learning-Based Gully Erosion Susceptibility Mapping: A Case Study of Eastern IndiaSunil Saha, Jagabandhu Roy, Alireza Arabameri, et al.
The Science of the Total Environment|December 22, 2018
GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approachesAlireza Arabameri, Khalil Rezaei, Artemi Cerda, et al.
The Science of the Total Environment|July 1, 2019
Novel ensembles of COPRAS multi-criteria decision-making with logistic regression, boosted regression tree, and random forest for spatial prediction of gully erosion susceptibilityAlireza Arabameri, Mojtaba Yamani, Biswajeet Pradhan, et al.
The Science of the Total Environment|May 14, 2020
Predicting the deforestation probability using the binary logistic regression, random forest, ensemble rotational forest, REPTree: A case study at the Gumani River Basin, IndiaSunil Saha, Mantosh Saha, Kaustuv Mukherjee, et al.
Pageof 3