Prediction of dissolved oxygen in River Calder by noise elimination time series using wavelet transform

Abstract

Prediction of dissolved oxygen (DO) plays an important role in water resources especially in surface waters such as rivers. The oxygen affects a vast number of other water indicators. In this study, the artificial neural network (ANN) and a hybrid wavelet-ANN (WANN) models were considered to predict thirty minutes dissolved oxygen in the River Calder at the Methley Bridge Station was located in the UK. For the proposed WANN model, the discrete wavelet transform (DWT) was linked to the ANN model for DO prediction. To achieve this aim, the original time series of thirty minutes DO and temperature (T) were decomposed in several sub-time series by DWT, and these new sub-series were imposed to the ANN model. The results were compared with single ANN model. The comparisons were done by some of the widely used relevant physical statistic indices. The Nash–Sutcliffe coefficient values were 0.998 and 0.740 for the WANN and ANN models, respectively. The model computed values of DO by the WANN model were in close agreement with respective measured values in the river water. Elimination noise by DWT model during pre-processing data is one of the abilities of the WANN model to better prediction. Since the results indicate closer approximation of the peak DO values by the WANN model, this model could be used for the simulation of cumulative DO data prediction in thirty minutes ahead.