Enhancing flood-prone area mapping: fine-tuning the K-nearest neighbors (KNN) algorithm for spatial modelling-Reference-Cited by-同舟云学术

Enhancing flood-prone area mapping: fine-tuning the K-nearest neighbors (KNN) algorithm for spatial modelling

Published:2024-03-04 Issue:1 Volume:17 Page:
ISSN:1753-8947
Container-title:International Journal of Digital Earth
language:en
Short-container-title:International Journal of Digital Earth

Author:

Razavi-Termeh Seyed Vahid¹,Sadeghi-Niaraki Abolghasem¹,Razavi Saman²,Choi Soo-Mi¹

Affiliation:

1. Department of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Republic of Korea

2. Global Institute for Water Security, School of Environmental and Sustainability, University of Saskatchewan, Saskatoon, Canada

Funder

ITRC Support Program

Metaverse Support Program to Nurture the Best Talents

Ministry of Science and ICT of Korea

Institute of Information and Communications Technology Planning and Evaluation

Ministry of Trade, Industry and Energy

Korea Institute for Advancement of Technology

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2024.2311325

Reference103 articles.

1. A new feature selection method to improve the document clustering using particle swarm optimization algorithm

2. Assessment Analysis of Flood Susceptibility in Tropical Desert Area: A Case Study of Yemen

3. Computational Machine Learning Approach for Flood Susceptibility Assessment Integrated with Remote Sensing and GIS Techniques from Jeddah, Saudi Arabia

4. Flood Analysis Using HEC-RAS and HEC-HMS: A Case Study of Khazir River (Middle East—Northern Iraq)

5. Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review

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