An artificial neural network to estimate the foliar and ground cover input variables of the Rangeland Hydrology and Erosion Model-Reference-Cited by-同舟云学术

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An artificial neural network to estimate the foliar and ground cover input variables of the Rangeland Hydrology and Erosion Model

Published:2024-03 Issue: Volume:631 Page:130835
ISSN:0022-1694
Container-title:Journal of Hydrology
language:en
Short-container-title:Journal of Hydrology

Author:

Saeedimoghaddam Mahmoud^ORCID,Nearing Grey^ORCID,Goodrich David C.^ORCID,Hernandez Mariano^ORCID,Guertin David Phillip^ORCID,Metz Loretta J.^ORCID,Wei Haiyan^ORCID,Ponce-Campos Guillermo^ORCID,Burns Shea^ORCID,McCord Sarah E.^ORCID,Nearing Mark A.,Williams C. Jason^ORCID,Houdeshell Carrie-Ann,Rahman Mashrekur^ORCID,Meles Menberu B.,Barker Steve

Publisher

Elsevier BV

Reference82 articles.

1. TensorFlow: Large-scale machine learning on heterogeneous systems;Abadi,2015

2. Rangeland canopy cover estimation using landsat OLI data and vegetation indices in Sabalan rangelands, Iran;Abdolalizadeh;Arab. J. Geosci.,2020

3. Copernicus DEM: Copernicus Digital Elevation Model Product Handbook;Airbus,2022

4. Rangeland hydrology and erosion model (RHEM) enhancements for applications on disturbed rangelands;Al-Hamdan;Hydrol. Process.,2015

5. A new integrated approach for landslide data balancing and spatial prediction based on generative adversarial networks (GAN);Al-Najjar;Remote Sens.,2021

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