Forecasting grace data over the African watersheds using artificial neural networks

Simple item page
dc.contributor.authorAhmed, Mohamed
dc.contributor.authorSultan, Mohamed
dc.contributor.authorTissot, Philippe
dc.creator.orcidhttps://orcid.org/0000-0001-7420-6579en_US
dc.creator.orcidhttps://orcid.org/0000-0002-2954-2378en_US
dc.creator.orcidhttps://orcid.org/0000-0001-7420-6579
dc.creator.orcidhttps://orcid.org/0000-0002-2954-2378
dc.creator.orcidhttps://orcid.org/0000-0001-7420-6579
dc.creator.orcidhttps://orcid.org/0000-0002-2954-2378https://orcid.org/0000-0001-7420-6579
dc.creator.orcidhttps://orcid.org/0000-0002-2954-2378
dc.date.accessioned2021-10-27T21:40:13Z
dc.date.available2021-10-27T21:40:13Z
dc.date.issued2019-07-27
dc.description.abstractThe GRACE-derived terrestrial water storage (TWSGRACE) provides measurements of the mass exchange and transport between continents, oceans, and ice sheets. In this study, a statistical approach was used to forecast TWSGRACE data using 10 major African watersheds as test sites. The forecasted TWSGRACE was then used to predict drought events in the examined African watersheds. Using a nonlinear autoregressive with exogenous input (NARX) model, relationships were derived between TWSGRACE data and the controlling and/or related variables (rainfall, temperature, evapotranspiration, and Normalized Difference Vegetation Index). The performance of the model was found to be “very good” (Nash–Sutcliffe (NSE) > 0.75; scaled root mean square error (R*) < 0.5) for 60% of the investigated watersheds, “good” (NSE > 0.65; R* < 0.6) for 10%, and “satisfactory” (NSE > 0.50; R* < 0.7) for the remaining 30% of the watersheds. During the forecasted period, no drought events were predicted over the Niger basin, the termination of the latest (March–October 2015) drought event was observed over the Zambezi basin, and the onset of a drought event (January-March 2016) over the Lake Chad basin was correctly predicted. Adopted methodologies generate continuous and uninterrupted TWSGRACE records, provide predictive tools to address environmental and hydrological problems, and help bridge the current gap between GRACE missions.en_US
dc.identifier.citationAhmed, M., Sultan, M., Elbayoumi, T. and Tissot, P., 2019. Forecasting grace data over the African watersheds using artificial neural networks. Remote Sensing, 11(15), p.1769.en_US
dc.identifier.doihttps://doi.org/10.3390/rs11151769
dc.identifier.urihttps://hdl.handle.net/1969.6/89875
dc.language.isoen_USen_US
dc.publisherMDPIen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectgraceen_US
dc.subjecttwsen_US
dc.subjectpredictionen_US
dc.subjectforecastingen_US
dc.subjectnarxen_US
dc.subjectdroughten_US
dc.subjectafricaen_US
dc.titleForecasting grace data over the African watersheds using artificial neural networksen_US
dc.typeArticleen_US

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
Ahmed_Mohamed_Remote-sensing.pdf
Size:
1.94 MB
Format:
Adobe Portable Document Format
Description:
Article
Download

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.72 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

COS Faculty Works