Mapping of the wind erodible fraction of soil by bidirectional gated recurrent unit (BiGRU) and bidirectional recurrent neural network (BiRNN) deep learning models-Reference-Cited by-同舟云学术

Mapping of the wind erodible fraction of soil by bidirectional gated recurrent unit (BiGRU) and bidirectional recurrent neural network (BiRNN) deep learning models

Published:2023-04 Issue: Volume:223 Page:106953
ISSN:0341-8162
Container-title:CATENA
language:en
Short-container-title:CATENA

Author:

Rezaei Mahrooz^ORCID,Mohammadifar Aliakbar,Gholami Hamid,Mina Monireh,Riksen Michel J.P.M.,Ritsema Coen

Publisher

Elsevier BV

Subject

Earth-Surface Processes

Reference53 articles.

1. Investigation of the 2018 Shiraz dust event: Potential sources of metals, rare earth elements, and radionuclides; health assessment;Abbasi;Chemosphere,2021

2. Abdelgwad, M.M., Soliman, T.H.A., Taloba, A.I., Farghaly, M.F., 2021. Arabic aspect based sentiment analysis using bidirectional GRU based models. J. King Saud Univ.-Comput. Inform. Sci.

3. Novel ensemble approach of deep learning neural network (DLNN) model and particle swarm optimization (PSO) algorithm for prediction of gully erosion susceptibility;Band;Sensors,2020

4. Trace-element concentrations and water-soluble ions in size-segregated dust-borne and soil samples in Sistan, southeast Iran;Behrooz;Aeolian Res.,2017

5. Regional study of no-till impacts on near-surface aggregate properties that influence soil erodibility;Blanco-Canqui;Soil Sci. Soc. Am. J.,2009

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