A comparative study of Machine Learning and Deep Learning methods for flood forecasting in the Far-North region, Cameroon-Reference-Cited by-同舟云学术

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A comparative study of Machine Learning and Deep Learning methods for flood forecasting in the Far-North region, Cameroon

Published:2024-03 Issue: Volume:23 Page:e02053
ISSN:2468-2276
Container-title:Scientific African
language:en
Short-container-title:Scientific African

Author:

Dtissibe Francis Yongwa^ORCID,Ari Ado Adamou Abba^ORCID,Abboubakar Hamadjam^ORCID,Njoya Arouna Ndam,Mohamadou Alidou,Thiare Ousmane

Publisher

Elsevier BV

Subject

Multidisciplinary

Reference87 articles.

1. World Disasters Report 2020 : Come Heat or High Water;IFRC,2020

2. Flood forecasting based on an artificial neural network scheme;Dtissibe;Nat. Hazards,2020

3. Artificial neural networks for flood susceptibility mapping in data-scarce urban areas;Falah,2019

4. Latest tools and methodologies for flood modeling;Simonov,2017

5. Global Assessment Report on Disaster Risk Reduction 2022: Our World at Risk: Transforming Governance for a Resilient Future;UNISDR,2022

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1. A systematic review of trustworthy artificial intelligence applications in natural disasters;Computers and Electrical Engineering;2024-09

2. Enhancing Flood Risk Mitigation by Advanced Data-Driven Approach;Heliyon;2024-09

3. River flood prediction through flow level modeling using multi-attention encoder-decoder-based TCN with filter-wrapper feature selection;Earth Science Informatics;2024-08-22

4. The technological assessment of green buildings using artificial neural networks;Heliyon;2024-08

5. A novel approach to forecast water table rise in arid regions using stacked ensemble machine learning and deep artificial intelligence models;Journal of Hydrology;2024-08

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