Monthly Predicted Flow Values of the Sanaga River in Cameroon Using Neural Networks Applied to GLDAS, MERRA and GPCP Data
Journal of Water Resources and Ocean Science
Volume 3, Issue 2, April 2014, Pages: 22-29
Received: Apr. 23, 2014; Accepted: May 23, 2014; Published: May 30, 2014
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Authors
SIDDI Tengeleng, Laboratoire d’Ingénierie des Systèmes Industriels et de l'Environnement (LISIE), Institut Universitaire de Technologie Fotso Victor, Université de Dschang, P.O Box 134 Bandjoun, Cameroun; Higher Institute of the Sahel, University of Maroua, P.O. Box 46 Maroua, Cameroon; Laboratory of Mechanics and Modeling of Physical Systems (L2MSP) - University of Dschang, Cameroon
NZEUKOU Armand, Laboratoire d’Ingénierie des Systèmes Industriels et de l'Environnement (LISIE), Institut Universitaire de Technologie Fotso Victor, Université de Dschang, P.O Box 134 Bandjoun, Cameroun
KAPTUE Armel, Geographic Information Science Center of Excellence, South Dakota State University, Brookling, SD 57007, USA
TCHAKOUTIO SANDJON Alain, Laboratoire d’Ingénierie des Systèmes Industriels et de l'Environnement (LISIE), Institut Universitaire de Technologie Fotso Victor, Université de Dschang, P.O Box 134 Bandjoun, Cameroun; Laboratory for Environmental Modeling and Atmospheric Physics (LAMEPA) Department of Physics University of Yaounde 1 P.O Box 812 Yaounde, Cameroon
SIMO Théophile, Laboratory of Automatic and Applied Informatics (LAIA), IUT-FV, University of Dschang
Djiongo Cedrigue, Laboratory of Automatic and Applied Informatics (LAIA), IUT-FV, University of Dschang
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Abstract
The aim of our study is to predict the discharge rate of the river Sanaga using neural network techniques. Our investigations have taken place in the Sanaga watershed area in Cameroon. The measurement station is situated in the locality of Edea-Song-Mbengue (04°04’15”N, 10°27’50”E) where we have obtained monthly values of the river Sanaga discharge rates that have been measured in situ from January 1989 to December 2004. We have trained neural networks (NN), each with data of parameters such as the surface albedo, the total cloud fraction, the evaporation, the outgoing longwave radiation, the air temperature, the specific humidity, the surface runoff and the precipitation height. The precipitation values have been obtained from GPCP (Global Precipitation Climatology Project) and those of the other parameters from the data assimilation systems GLDAS (Global Land Data Assimilation System) and MERRA (Modern Era-Retrospective analysis for Research and Application). As desired outputs of the NN during the learning process, we have used the measured river runoff values. After introducing temporal delays of 01 and 02 months in the learning-process, we could observe the presence of the memory effect of the parameters used on the temporal evolution of the river discharge rate. After analysis of the performance's criteria of the NN with the help of the calculated Root Means Square Errors (RMSE) and determination coefficients between predicted values and in situ observed ones, we have perceived that the NN which takes into account the two-month delay can predict the river discharge rate with a strong correlation.
Keywords
River Runoff, GLDAS, GPCP, MERRA, Neural Network, Sanaga Watershed area
To cite this article
SIDDI Tengeleng, NZEUKOU Armand, KAPTUE Armel, TCHAKOUTIO SANDJON Alain, SIMO Théophile, Djiongo Cedrigue, Monthly Predicted Flow Values of the Sanaga River in Cameroon Using Neural Networks Applied to GLDAS, MERRA and GPCP Data, Journal of Water Resources and Ocean Science. Vol. 3, No. 2, 2014, pp. 22-29. doi: 10.11648/j.wros.20140302.12
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