Comparative study of convolutional neural network (CNN) and support vector machine (SVM) for flood susceptibility mapping: a case study at Ras Gharib, Red Sea, Egypt-Reference-Cited by-同舟云学术

Comparative study of convolutional neural network (CNN) and support vector machine (SVM) for flood susceptibility mapping: a case study at Ras Gharib, Red Sea, Egypt

Published:2022-03-25 Issue:26 Volume:37 Page:11088-11115
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Youssef Ahmed M.¹²^ORCID,Pradhan Biswajeet³⁴^ORCID,Dikshit Abhirup³^ORCID,Mahdi Ali M.⁵

Affiliation:

1. Geology Department, Faculty of Science, South Valley University, Sohag, Egypt

2. Geological Hazards Center, Saudi Geological Survey, Jeddah, Kingdom of Saudi Arabia

3. Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW, Australia

4. Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

5. Geology Department, Faculty of Science, South Valley University, Qena, Egypt

Publisher

Informa UK Limited

Subject

Water Science and Technology,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2022.2046866

Reference138 articles.

1. A Hydrological and Geomorphometric Approach to Understanding the Generation of Wadi Flash Floods

2. Integrated Methodology for Urban Flood Risk Mapping at the Microscale in Ungauged Regions: A Case Study of Hurghada, Egypt

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