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Journal Abstract Search

249 related items for PubMed ID: 32199317

  • 1. Application of learning vector quantization and different machine learning techniques to assessing forest fire influence factors and spatial modelling.
    Pourghasemi HR, Gayen A, Lasaponara R, Tiefenbacher JP.
    Environ Res; 2020 May; 184():109321. PubMed ID: 32199317
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  • 2. GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran.
    Naghibi SA, Pourghasemi HR, Dixon B.
    Environ Monit Assess; 2016 Jan; 188(1):44. PubMed ID: 26687087
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  • 5. Fire-susceptibility mapping in the natural areas of Iran using new and ensemble data-mining models.
    Eskandari S, Pourghasemi HR, Tiefenbacher JP.
    Environ Sci Pollut Res Int; 2021 Sep; 28(34):47395-47406. PubMed ID: 33891241
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  • 8. Applying genetic algorithms to set the optimal combination of forest fire related variables and model forest fire susceptibility based on data mining models. The case of Dayu County, China.
    Hong H, Tsangaratos P, Ilia I, Liu J, Zhu AX, Xu C.
    Sci Total Environ; 2018 Jul 15; 630():1044-1056. PubMed ID: 29554726
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  • 10. Machine learning approaches in GIS-based ecological modeling of the sand fly Phlebotomus papatasi, a vector of zoonotic cutaneous leishmaniasis in Golestan province, Iran.
    Mollalo A, Sadeghian A, Israel GD, Rashidi P, Sofizadeh A, Glass GE.
    Acta Trop; 2018 Dec 15; 188():187-194. PubMed ID: 30201488
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  • 12. Assessing the performance of GIS- based machine learning models with different accuracy measures for determining susceptibility to gully erosion.
    Garosi Y, Sheklabadi M, Conoscenti C, Pourghasemi HR, Van Oost K.
    Sci Total Environ; 2019 May 10; 664():1117-1132. PubMed ID: 30901785
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  • 17. Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms.
    Gayen A, Pourghasemi HR, Saha S, Keesstra S, Bai S.
    Sci Total Environ; 2019 Jun 10; 668():124-138. PubMed ID: 30851678
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  • 19. Integration of hard and soft supervised machine learning for flood susceptibility mapping.
    Andaryani S, Nourani V, Haghighi AT, Keesstra S.
    J Environ Manage; 2021 Aug 01; 291():112731. PubMed ID: 33962279
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  • 20. A machine learning framework for multi-hazards modeling and mapping in a mountainous area.
    Yousefi S, Pourghasemi HR, Emami SN, Pouyan S, Eskandari S, Tiefenbacher JP.
    Sci Rep; 2020 Jul 22; 10(1):12144. PubMed ID: 32699313
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