Monthly Predicted Flow Values of the Sanaga River in Cameroon Using Neural Networks Applied to GLDAS, MERRA and GPCP Data

SIDDI Tengeleng; NZEUKOU Armand; KAPTUE Armel; TCHAKOUTIO SANDJON Alain; SIMO Théophile; Djiongo Cedrigue

doi:doi:10.11648/j.wros.20140302.12

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Monthly Predicted Flow Values of the Sanaga River in Cameroon Using Neural Networks Applied to GLDAS, MERRA and GPCP Data

SIDDI Tengeleng, NZEUKOU Armand, KAPTUE Armel, TCHAKOUTIO SANDJON Alain, SIMO Théophile, Djiongo Cedrigue

Published in Journal of Water Resources and Ocean Science (Volume 3, Issue 2)

Received: 23 April 2014 Accepted: 23 May 2014 Published: 30 May 2014

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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.

Published in	Journal of Water Resources and Ocean Science (Volume 3, Issue 2)
DOI	10.11648/j.wros.20140302.12
Page(s)	22-29
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2014. Published by Science Publishing Group

Keywords

River Runoff, GLDAS, GPCP, MERRA, Neural Network, Sanaga Watershed area

References

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APA Style

SIDDI Tengeleng, NZEUKOU Armand, KAPTUE Armel, TCHAKOUTIO SANDJON Alain, SIMO Théophile, et al. (2014). 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, 3(2), 22-29. https://doi.org/10.11648/j.wros.20140302.12

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ACS Style

SIDDI Tengeleng; NZEUKOU Armand; KAPTUE Armel; TCHAKOUTIO SANDJON Alain; SIMO Théophile, et al. Monthly Predicted Flow Values of the Sanaga River in Cameroon Using Neural Networks Applied to GLDAS, MERRA and GPCP Data. J. Water Resour. Ocean Sci. 2014, 3(2), 22-29. doi: 10.11648/j.wros.20140302.12

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AMA Style

SIDDI Tengeleng, NZEUKOU Armand, KAPTUE Armel, TCHAKOUTIO SANDJON Alain, SIMO Théophile, et al. Monthly Predicted Flow Values of the Sanaga River in Cameroon Using Neural Networks Applied to GLDAS, MERRA and GPCP Data. J Water Resour Ocean Sci. 2014;3(2):22-29. doi: 10.11648/j.wros.20140302.12

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@article{10.11648/j.wros.20140302.12,
  author = {SIDDI Tengeleng and NZEUKOU Armand and KAPTUE Armel and TCHAKOUTIO SANDJON Alain and SIMO Théophile and Djiongo Cedrigue},
  title = {Monthly Predicted Flow Values of the Sanaga River in Cameroon Using Neural Networks Applied to GLDAS, MERRA and GPCP Data},
  journal = {Journal of Water Resources and Ocean Science},
  volume = {3},
  number = {2},
  pages = {22-29},
  doi = {10.11648/j.wros.20140302.12},
  url = {https://doi.org/10.11648/j.wros.20140302.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wros.20140302.12},
  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.},
 year = {2014}
}

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TY  - JOUR
T1  - Monthly Predicted Flow Values of the Sanaga River in Cameroon Using Neural Networks Applied to GLDAS, MERRA and GPCP Data
AU  - SIDDI Tengeleng
AU  - NZEUKOU Armand
AU  - KAPTUE Armel
AU  - TCHAKOUTIO SANDJON Alain
AU  - SIMO Théophile
AU  - Djiongo Cedrigue
Y1  - 2014/05/30
PY  - 2014
N1  - https://doi.org/10.11648/j.wros.20140302.12
DO  - 10.11648/j.wros.20140302.12
T2  - Journal of Water Resources and Ocean Science
JF  - Journal of Water Resources and Ocean Science
JO  - Journal of Water Resources and Ocean Science
SP  - 22
EP  - 29
PB  - Science Publishing Group
SN  - 2328-7993
UR  - https://doi.org/10.11648/j.wros.20140302.12
AB  - 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.
VL  - 3
IS  - 2
ER  -

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Author Information

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
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
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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Plain Text BibTeX RIS

APA Style

SIDDI Tengeleng, NZEUKOU Armand, KAPTUE Armel, TCHAKOUTIO SANDJON Alain, SIMO Théophile, et al. (2014). 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, 3(2), 22-29. https://doi.org/10.11648/j.wros.20140302.12

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ACS Style

SIDDI Tengeleng; NZEUKOU Armand; KAPTUE Armel; TCHAKOUTIO SANDJON Alain; SIMO Théophile, et al. Monthly Predicted Flow Values of the Sanaga River in Cameroon Using Neural Networks Applied to GLDAS, MERRA and GPCP Data. J. Water Resour. Ocean Sci. 2014, 3(2), 22-29. doi: 10.11648/j.wros.20140302.12

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AMA Style

SIDDI Tengeleng, NZEUKOU Armand, KAPTUE Armel, TCHAKOUTIO SANDJON Alain, SIMO Théophile, et al. Monthly Predicted Flow Values of the Sanaga River in Cameroon Using Neural Networks Applied to GLDAS, MERRA and GPCP Data. J Water Resour Ocean Sci. 2014;3(2):22-29. doi: 10.11648/j.wros.20140302.12

Copy | Download

@article{10.11648/j.wros.20140302.12,
  author = {SIDDI Tengeleng and NZEUKOU Armand and KAPTUE Armel and TCHAKOUTIO SANDJON Alain and SIMO Théophile and Djiongo Cedrigue},
  title = {Monthly Predicted Flow Values of the Sanaga River in Cameroon Using Neural Networks Applied to GLDAS, MERRA and GPCP Data},
  journal = {Journal of Water Resources and Ocean Science},
  volume = {3},
  number = {2},
  pages = {22-29},
  doi = {10.11648/j.wros.20140302.12},
  url = {https://doi.org/10.11648/j.wros.20140302.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wros.20140302.12},
  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.},
 year = {2014}
}

Copy | Download

TY  - JOUR
T1  - Monthly Predicted Flow Values of the Sanaga River in Cameroon Using Neural Networks Applied to GLDAS, MERRA and GPCP Data
AU  - SIDDI Tengeleng
AU  - NZEUKOU Armand
AU  - KAPTUE Armel
AU  - TCHAKOUTIO SANDJON Alain
AU  - SIMO Théophile
AU  - Djiongo Cedrigue
Y1  - 2014/05/30
PY  - 2014
N1  - https://doi.org/10.11648/j.wros.20140302.12
DO  - 10.11648/j.wros.20140302.12
T2  - Journal of Water Resources and Ocean Science
JF  - Journal of Water Resources and Ocean Science
JO  - Journal of Water Resources and Ocean Science
SP  - 22
EP  - 29
PB  - Science Publishing Group
SN  - 2328-7993
UR  - https://doi.org/10.11648/j.wros.20140302.12
AB  - 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.
VL  - 3
IS  - 2
ER  -

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