Interpretation of Bayesian-optimized deep learning models for enhancing soil erosion susceptibility prediction and management: a case study of Eastern India-Reference-Cited by-同舟云学术

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Interpretation of Bayesian-optimized deep learning models for enhancing soil erosion susceptibility prediction and management: a case study of Eastern India

Published:2024-01 Issue:1 Volume:39 Page:
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Alkahtani Meshel¹,Mallick Javed¹,Alqadhi Saeed¹,Sarif Md Nawaj²,Fatahalla Mohamed Ahmed Mohamed³,Abdo Hazem Ghassan⁴^ORCID

Affiliation:

1. Department of Civil Engineering, College of Engineering, King Khalid University, Abha, Saudi Arabia

2. Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India

3. Department of Social Sciences, Faculty of Arts, University of Hail, Hail, Saudi Arabia

4. Geography Department, Faculty of Arts and Humanities, Tartous University, Tartous, Syria

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2024.2367611

Reference69 articles.

1. Estimating water erosion using RUSLE, GIS and remote sensing in Wadi-Qandeel river basin, Lattakia, Syria

2. Mapping the soil loss in Marqya basin: Syria using RUSLE model in GIS and RS techniques

3. Quantifying soil erosion and influential factors in Guwahati's urban watershed using statistical analysis, machine and deep learning

4. Contribution and behavioral assessment of physical and anthropogenic factors for soil erosion using integrated deep learning and game theory

5. A new framework to identify most suitable priority areas for soil-water conservation using coupling mechanism in Guwahati urban watershed, India, with future insight

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