Water Quality Forecast Based on BP-Artificial Neural Network Model in Qiantang River

Jin Ting Ding; Jie He

doi:10.4028/www.scientific.net/AMM.668-669.994

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 668-669Water Quality Forecast Based on BP-Artificial...

Water Quality Forecast Based on BP-Artificial Neural Network Model in Qiantang River

Abstract:

This study aims at providing a back propagation-artificial neural network (BP-ANN) model on forecasting the water quality change trend of Qiantang River basin. To achieve this goal, a three-layer (one input layer, one hidden layer, and one output layer) BP-ANN with the LM regularization training algorithm was used. Water quality variables such as pH value, dissolved oxygen, permanganate index and ammonia-nitrogen was selected as the input data to obtain the output of the neural network. The ANN structure with 17 hidden neurons obtained the best selection. The comparison between the original measured and forecast values of the ANN model shows that the relative errors, with a few exceptions, were lower than 9%. The results indicated that the BP neural network can be satisfactorily applied to forecast precise water quality parameters and is suitable for pre-alarm of water quality trend.

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Periodical:

Applied Mechanics and Materials (Volumes 668-669)

Pages:

994-998

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.668-669.994

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Online since:

October 2014

Authors:

Jin Ting Ding*, Jie He

Keywords:

Artificial Neural Network (ANN), Model, Qiantang River, Water Quality Forecast

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