Abstract

Forecasting water quality is always an effective approach for water environmental management. This study presents a combined Wavelet transform (WA) and Artificial Neural Network (ANN) model for monthly ammonia nitrogen series prediction in river water. The WA decomposed original time series into different subseries, in which the most significant one was chosen as the training data instead of the original series. Compared to the traditional ANN, the WA-ANN models were found more accurate and reliable. The results of the study indicate that WA could remove the noise of the original datasets and the WA-ANN could help environment decision-maker manage water quality more effective.

Keywords:

Artificial neural network, environment management, Harbin region, time series, water quality forecasting, wavelet analysis,

References

Competing interests

The authors have no competing interests.

Open Access Policy

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Copyright

The authors have no competing interests.