Prediction of Monthly Dissolved Oxygen Using Wavelet and Artificial Neural Network Combined Model

Qualitative and quantitative management of water resources to meet the demand for different usages is the major approach in each country policy. In this regard, dam reservoirs water quality monitoring is an important step in the management of these resources. This study investigated the prediction of dissolved oxygen in a gauging station in the Boulder reservoir (USA) by artificial neural network, multi linear regression and conjunction of wavelet analysis and artificial neural network models. In the proposed wavelet analysis and artificial neural network model, observed time series of dissolved oxygen was decomposed at different scales by wavelet analysis. Then, total effective time series of this water quality index was imposed as inputs to the artificial neural network model for prediction of dissolved oxygen in one month ahead. Results showed that the wavelet analysis and artificial neural network combined model performance were better in prediction rather than the artificial neural network and multi linear regression models. Using wavelet analysis improved the modeling results considerably. In the combined model, determination coefficient, E, and RMSE is obtained 0.96 and 0.22 respectively. Artificial neural network and the combined wavelet with artificial neural network models produced reasonable predictions for the minimum values that lead anaerobic condition in reservoir.