American Journal of Water Resources
ISSN (Print): 2333-4797 ISSN (Online): 2333-4819 Website: http://www.sciepub.com/journal/ajwr Editor-in-chief: Apply for this position
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American Journal of Water Resources. 2016, 4(2), 35-43
DOI: 10.12691/ajwr-4-2-2
Open AccessArticle

Prediction of Missing Hydro-Meteorological Data Series Using Artificial Neural Networks (ANN) for Upper Tana River Basin, Kenya

Raphael M. Wambua1, , Benedict M. Mutua1 and James M. Raude2

1Egerton University Department of Agricultural Engineering, Kenya

2Jomo Kenyatta University of Agriculture and Technology, SWEED, Kenya

Pub. Date: May 25, 2016

Cite this paper:
Raphael M. Wambua, Benedict M. Mutua and James M. Raude. Prediction of Missing Hydro-Meteorological Data Series Using Artificial Neural Networks (ANN) for Upper Tana River Basin, Kenya. American Journal of Water Resources. 2016; 4(2):35-43. doi: 10.12691/ajwr-4-2-2

Abstract

Accurate prediction of missing hydro-meteorological data is crucial in planning, design, development and management of water resources systems. In the present research, prediction of such data using Artificial Neural Networks (ANN) based on temporal and spatial auto-correlation has been conducted for upper Tana River basin in Kenya. Different ANN models were formulated using a combination of numerous data delays in the ANN input layer. The findings show that the best models comprise of a feed-forward neural network trained on Levenberg-Marquardt algorithm with single hidden layer. Additionally, the best ANN architecture model for predicting missing stream flow data was at gauge station 4CC03 with correlation coefficient and MSE of 0732 and 0.242 respectively during validation. Temporal auto-correlation of the observed and the predicted stream flow values were evaluated using a correlation coefficient R that resulted to highest value of 0.756 at gauge station 4AB05. The best ANN model for prediction of missing precipitation data was at station 9037112 with R value of 0.970. In both cases the best performance was at epochs 9 and 20 respectively. The spatial auto-correlation show that the best ANN architecture model for prediction of missing stream flow data was at gauge station 4CC03 with R value of 0.723, while the one for precipitation was at station 9037096 with R value of 0.712 during the validation. The results indicate that the spatial auto-correlation of hydro-meteorological data using ANN is better than the temporal auto-correlation in the data prediction in upper Tana River basin.

Keywords:
Prediction hydro-meteorological data ANN data delay auto-correlation upper Tana River basin

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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