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Information 2014, 5(4), 570-586; doi:10.3390/info5040570

Forecasting Hoabinh Reservoir’s Incoming Flow: An Application of Neural Networks with the Cuckoo Search Algorithm

1
Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung 40724, Taiwan
2
Department of Electrical and Electronic Engineering, University of Transport Technology, Hanoi 100000, Vietnam
*
Author to whom correspondence should be addressed.
Received: 16 July 2014 / Revised: 15 September 2014 / Accepted: 29 October 2014 / Published: 5 November 2014
(This article belongs to the Section Information Applications)
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Abstract

The accuracy of reservoir flow forecasting has the most significant influence on the assurance of stability and annual operations of hydro-constructions. For instance, accurate forecasting on the ebb and flow of Vietnam’s Hoabinh Reservoir can aid in the preparation and prevention of lowland flooding and drought, as well as regulating electric energy. This raises the need to propose a model that accurately forecasts the incoming flow of the Hoabinh Reservoir. In this study, a solution to this problem based on neural network with the Cuckoo Search (CS) algorithm is presented. In particular, we used hydrographic data and predicted total incoming flows of the Hoabinh Reservoir over a period of 10 days. The Cuckoo Search algorithm was utilized to train the feedforward neural network (FNN) for prediction. The algorithm optimized the weights between layers and biases of the neuron network. Different forecasting models for the three scenarios were developed. The constructed models have shown high forecasting performance based on the performance indices calculated. These results were also compared with those obtained from the neural networks trained by the particle swarm optimization (PSO) and back-propagation (BP), indicating that the proposed approach performed more effectively. Based on the experimental results, the scenario using the rainfall and the flow as input yielded the highest forecasting accuracy when compared with other scenarios. The performance criteria RMSE, MAPE, and R obtained by the CS-FNN in this scenario were calculated as 48.7161, 0.067268 and 0.8965, respectively. These results were highly correlated to actual values. It is expected that this work may be useful for hydrographic forecasting. View Full-Text
Keywords: Cuckoo Search algorithm; artificial neural network; prediction; flow forecasting; reservoir Cuckoo Search algorithm; artificial neural network; prediction; flow forecasting; reservoir
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Chen, J.-F.; Hsieh, H.-N.; Do, Q.H. Forecasting Hoabinh Reservoir’s Incoming Flow: An Application of Neural Networks with the Cuckoo Search Algorithm. Information 2014, 5, 570-586.

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