Detection of areas prone to flood-induced landslides risk using certainty factor and its hybridization with FAHP, XGBoost and deep learning neural network-Reference-Cited by-同舟云学术

Detection of areas prone to flood-induced landslides risk using certainty factor and its hybridization with FAHP, XGBoost and deep learning neural network

Published:2021-09-08 Issue: Volume: Page:1-36
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Costache Romulus¹²³^ORCID,Ali Sk Ajim⁴^ORCID,Parvin Farhana⁴^ORCID,Pham Quoc Bao⁵,Arabameri Alireza⁶^ORCID,Nguyen Hoang⁷^ORCID,Crăciun Anca²,Anh Duong Tran⁸

Affiliation:

1. Department of Civil Engineering, Transilvania University of Brasov, Brasov, Romania

2. Danube Delta National Institute for Research and Development, Tulcea, Romania

3. National Institute of Hydrology and Water Management, Bucharest, Romania

4. Department of Geography, Faculty of Science, Aligarh Muslim University (AMU), Aligarh, Uttar Pradesh, India

5. Institute of Applied Technology, Thu Dau Mot University, Thu Dau Mot City, Vietnam

6. Department of Geomorphology, Tarbiat Modares University, Tehran, Iran

7. Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, Hanoi, Vietnam

8. Institute of Applied Science, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Vietnam

Funder

Romanian Ministry of Education and Research, CNCS – UEFISCDI

Publisher

Informa UK Limited

Subject

Water Science and Technology,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2021.1973115

Reference95 articles.

1. Mitigation of Scour Failure Risk of a River Bridge Located in an Ungauged Basin

2. Akay H. 2021b. Flood hazards susceptibility mapping using statistical, fuzzy logic, and MCDM methods. Soft Comput. 25(14):9325–9346.

3. Spatial modeling of snow avalanche susceptibility using hybrid and ensemble machine learning techniques

4. Application of GIS-based analytic hierarchy process and frequency ratio model to flood vulnerable mapping and risk area estimation at Sundarban region, India

5. GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, naïve Bayes tree, bivariate statistics and logistic regression: A case of Topľa basin, Slovakia

Cited by 24 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Sample size effects on landslide susceptibility models: A comparative study of heuristic, statistical, machine learning, deep learning and ensemble learning models with SHAP analysis;Computers & Geosciences;2024-09

2. Flood risk assessment of urban metro system using random forest algorithm and triangular fuzzy number based analytical hierarchy process approach;Sustainable Cities and Society;2024-08

3. Flash flood risk mapping using Analytic Hierarchy Process and machine learning: case of Souk-Ahras City, Northeastern Algeria;2024-06-26

4. Optimization of Highway Performance and Safety by Integrated Multi-Criteria Decision-Making Techniques;International Journal of Pavement Research and Technology;2024-06-22

5. Solving flood problems with deep learning technology: Research status, strategies, and future directions;Sustainable Development;2024-06-08