| Popis: |
Water Quality (WQ) modeling and forecasting are very challenging for water management bodies due to the complex and nonlinear relationship between the parameters responsible for determining water quality. The main focus of this paper is the water quality prediction of the Ganges River by analyzing the impact of one of the critical configuration parameters of a neural network known as the learning rate. The proposed prediction model based on an artificial neural network (ANN) consists of different sets of experiments performed by comparing twelve different training functions against the variation in learning rates. A total of 360 experiments have been conducted on the dataset collected over the period 2001 to 2015 with five stations along the Ganges River in the state of Uttarakhand, India. All experiments have been conducted in MATLAB software. The ANN-based program is written in Matlab’s NN-Toolbox. As input parameters, we have used temperature, pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), and total coliform. The water quality standard set by the Central Pollution Control Board of India has been used. The performance of the developed model has been calculated based on Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE). Trail training function-based artificial neural network models indicate higher predictive accuracy when compared to other models developed using the remaining eleven training functions when the learning rate is set to 0.04. In conclusion, ANN has the ability to efficiently predict the water quality of rivers and the learning rate has a greater impact on the development of such predictive models. So, it is required to be tuned very carefully. |
