Time Series Analysis Of Water Quality Prediction Model For Rivers Using Machine Learning Approach

Document Type : Primary Research paper

Authors

1 Computer Science & Information Technology, Mahatma Jyotibha Phule Rohilkhand University, Bareilly, U.P. India

2 Deptt. Of Environment Science, Gurukul Kangri University, Haridwar, Uttarakhand, India

3 Deptt. Of Computer Science, Uttarakhand Open University, Uttarakhand, India

Abstract

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 development and time series analysis of the water quality prediction model of the Ganges River based on one of the significant machine learning (ML) approach known as Artificial Neural Network (ANN). The impact of one of the critical configuration parameter of neural network known as learning rate was analyzed. 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). Trainlm 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 water quality of rivers and the learning rate has a greater impact in the development of such predictive models. So, it is required to be tuned very carefully. Overall, the machine learning approach, ANN proved to be successful in the time series analysis of WQ prediction model.

Keywords

Int. J. of Aquatic Science
Volume 12, Issue 2 - Serial Number 2
June 2021
Pages 2099-2114

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