Delineation of flood risk terrains and rainfall visualisation in the North Western part of Ghana-Reference-Cited by-同舟云学术

Delineation of flood risk terrains and rainfall visualisation in the North Western part of Ghana

Published:2024-05-16 Issue:3 Volume:10 Page:4567-4594
ISSN:2363-6203
Container-title:Modeling Earth Systems and Environment
language:en
Short-container-title:Model. Earth Syst. Environ.

Author:

Dekongmen Benjamin Wullobayi^ORCID,Kabo-bah Amos Tiereyangn,Anornu Geophrey Kwame,Akpoti Komlavi,Amo-Boateng Mark,Antwi Eric Ofosu,Boamah Edward Oppong,Sunkari Emmanuel Daanoba

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s40808-024-02041-z.pdf

Reference200 articles.

1. Abdrabo KI, Kantoush SA, Esmaiel A, Saber M, Sumi T, Almamari M, Elboshy B, Ghoniem S et al (2023) An integrated indicator-based approach for constructing an urban flood vulnerability index as an urban decision-making tool using the PCA and AHP techniques: a case study of Alexandria, Egypt. Urban Clim 48:1–19. https://doi.org/10.1016/j.uclim.2023.101426

2. Abdul-ganiyu S, Kpiebaya P (2020) Groundwater resources as a resilient alternative for irrigation in the Upper West Region of Ghana – a study of groundwater potential. Int J Innov Sci Res Tech 5:1–10

3. Abu M, Zango MS, Kazapoe R (2024) Controls of groundwater mineralization assessment in a mining catchment in the Upper West Region, Ghana: Insights from hydrochemistry, pollution indices of groundwater, and multivariate statistics. Innov Green Dev 3:1–14. https://doi.org/10.1016/j.igd.2023.100099

4. Addis A (2022) GIS – based flood susceptibility mapping using frequency ratio and information value models in Upper Abay River Basin, Ethiopia. SSRN Electron J 3:247–256. https://doi.org/10.2139/ssrn.4246701

5. Adel S, Fazel A, Mirfenderesk H, Tomlinson R, Blumenstein M (2014) Estuarine flood modelling using artificial neural networks Estuarine flood modelling using Artificial Neural Networks. in 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, Beijing, China pp 631–637. https://doi.org/10.1109/IJCNN.2014.6889704