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Article

Forecasting GRACE Data over the African Watersheds Using Artificial Neural Networks

1
Department of Physical and Environmental Sciences, Texas A&M University-Corpus Christi, 6300 Ocean Drive, Corpus Christi, TX 78412, USA
2
Department of Geological and Environmental Sciences, Western Michigan University, 1903 West Michigan Avenue, Kalamazoo, MI 49008, USA
3
Department of Mathematics, North Carolina A&T State University, Greensboro, NC 27411, USA
4
Conrad Blucher Institute for Surveying and Science, Texas A&M University-Corpus Christi, Corpus Christi, TX 78412, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(15), 1769; https://doi.org/10.3390/rs11151769
Received: 26 May 2019 / Revised: 4 July 2019 / Accepted: 26 July 2019 / Published: 27 July 2019
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
The 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. View Full-Text
Keywords: GRACE; TWS; prediction; forecasting; NARX; drought; Africa GRACE; TWS; prediction; forecasting; NARX; drought; Africa
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MDPI and ACS Style

Ahmed, M.; Sultan, M.; Elbayoumi, T.; Tissot, P. Forecasting GRACE Data over the African Watersheds Using Artificial Neural Networks. Remote Sens. 2019, 11, 1769. https://doi.org/10.3390/rs11151769

AMA Style

Ahmed M, Sultan M, Elbayoumi T, Tissot P. Forecasting GRACE Data over the African Watersheds Using Artificial Neural Networks. Remote Sensing. 2019; 11(15):1769. https://doi.org/10.3390/rs11151769

Chicago/Turabian Style

Ahmed, Mohamed, Mohamed Sultan, Tamer Elbayoumi, and Philippe Tissot. 2019. "Forecasting GRACE Data over the African Watersheds Using Artificial Neural Networks" Remote Sensing 11, no. 15: 1769. https://doi.org/10.3390/rs11151769

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