Forecasting water quality time series with a hybrid methodology

Typical time series prediction methods used in many real-world applications. The proposed approach consists of an ARIMA methodology and feed-forward, backpropagation network structure with an optimized conjugated training algorithm. The hybrid approach for time series prediction is tested using 144-month observations of water quality data, including water temperature, Total dissolved solids (TDS) and Sodium adsorption ratio (SAR), during 1997–2008 at Karun river, Iran. The correlation coefficients between the hybrid model predicted values and observed data for TDS, SAR and water temperature are 0.935, 0.939, and 0.892, respectively, which are satisfactory in common model applications. Predicted water quality data from the hybrid model are compared with those from the ARIMA methodology and neural network architecture using the accuracy measures.

Neural networks,ARIMA,Hybrid model,Time series