Forecasting GRACE Data over the African Watersheds Using Artificial Neural Networks

Mohamed Ahmed, Mohamed Sultan, Tamer M. Elbayoumi, Philippe E. Tissot

Remote Sensing 2019 article cited by 79

Source Open-access PDF More in Climate & Environment

importance 6/10 partially solved Geophysics and Gravity Measurements water resources stage: research

Problem

GRACE-derived terrestrial water storage needed to be forecast to predict drought events across 10 major African watersheds.

Why it matters

Drought forecasting supports water resource planning in African watersheds.

What the AI did

A NARX neural network derived relationships between GRACE water storage and rainfall, temperature, evapotranspiration and NDVI to forecast TWS and predict drought events.

Results

Very good (NSE>0.75, R*<0.5) for 60% of watersheds; good (NSE>0.65, R*<0.6) for 10%; satisfactory (NSE>0.50, R*<0.7) for remaining 30%

Impact

All ten watersheds achieved at least satisfactory forecast performance, supporting drought prediction use.

Record

Venue	Remote Sensing
Published	2019
Work type	article
Authors	Mohamed Ahmed, Mohamed Sultan, Tamer M. Elbayoumi, Philippe E. Tissot
Citations	79 (OpenAlex, at import time)
References	132
Model or method	NARX (nonlinear autoregressive with exogenous input) neural network
Organization	not stated in source
Primary source	https://doi.org/10.3390/rs11151769
Open access	gold
Keywords	GRACE, terrestrial water storage, drought forecasting, NARX, neural networks

Importance rating

6 / 10

significant — thousands to millions affected

Drought forecasting supports water resource planning across major African watersheds; abstract demonstrates correct drought onset/termination predictions but no population impact stated.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: 10 major African watersheds. Method and limits.

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