Modeling and Prediction of Groundwater Level Fluctuations Using Geoinformatics and Artificial Neural Networks in Al Ain City, UAE-Reference-Cited by-同舟云学术

Modeling and Prediction of Groundwater Level Fluctuations Using Geoinformatics and Artificial Neural Networks in Al Ain City, UAE

Published:2023 Issue: Volume: Page:205-217
ISSN:0921-092X
Container-title:Water Resources Management and Sustainability
language:
Short-container-title:

Author:

ElHaj Khalid,Issa Salem,Alshamsi Dalal,Cherkose Biruk Abera

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-24506-0_13

Reference27 articles.

1. Coulibaly P, Anctil F, Aravena R, Bobée B (2001) Artificial neural network modeling of water table depth fluctuations. Water Resour Res 37(4):885–896. https://doi.org/10.1029/2000WR900368

2. Elshorbagy A, Corzo G, Srinivasulu S, Solomatine DP (2010a) Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology—part 1: concepts and methodology. Hydrol Earth Syst Sci 14(10):1931–1941. https://doi.org/10.5194/hess-14-1931-2010

3. Elshorbagy A, Corzo G, Srinivasulu S, Solomatine DP (2010b) Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology—part 2: application. Hydrol Earth Syst Sci 14(10):1943–1961. https://doi.org/10.5194/hess-14-1943-2010

4. Environment Agency Abu Dhabi (2018) Groundwater atlas of Abu Dhabi Emirate, p 118. https://www.ead.gov.ae/storage/Post/files/276ac7243c429b04c1d781f1ae42d09f.pdf

5. Goossens RGS, De Dapper M, Ghabour TK, El Badawi M, Gad A-A (1993) Development of a GIS simulation model and the use of remote sensing for monitoring and prediction of soil salinity and waterlogging in the Nile delta (Egypt). 1941:137–150. https://doi.org/10.1117/12.154682