Prediction of River Water Quality Based on Artificial Neural Network

Danial Mustaqim Azmi; Norlina Mohd Sabri; Nik Marsyahariani Nik Daud; Nor Azila Awang Abu Bakar

Prediction of River Water Quality Based on Artificial Neural Network

Authors

Danial Mustaqim Azmi College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Terengganu Branch, Kuala Terengganu Campus, Terengganu, Malaysia
Norlina Mohd Sabri College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Terengganu Branch, Kuala Terengganu Campus, Terengganu, Malaysia
Nik Marsyahariani Nik Daud College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Terengganu Branch, Kuala Terengganu Campus, Terengganu, Malaysia
Nor Azila Awang Abu Bakar College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Terengganu Branch, Kuala Terengganu Campus, Terengganu, Malaysia

Keywords:

Water Analysis, River Quality, Prediction, Artificial neural network

Abstract

In machine learning, prediction is a method that is supported by historical data and is often used in various fields. It can be used to predict quality for water taken from river, which is a major source of life particularly for human. Water contamination may be a result of civilization and the rapid increment in economy. This research looks at how the Artificial Neural Network (ANN) algorithm predicts the Water Quality Index that will aid the environmental agencies and consumers. This study also aims to create an ANN-based water quality index prediction system that is effective at managing noisy data. Furthermore, the constructed system’s ability to accurately predict water quality is assessed. The water quality prediction method is based on the data taken from the three main rivers in Selangor, namely Sungai Buloh, Langat, and Kuala Selangor. The prediction takes into account variables such as Biological Oxygen Demand (BOD), dissolve oxygen (DO) and seven other water characteristics. The performance metric used in the study is the calculation of the accuracy for factors such as the number of neurons in the hidden layer, the epoch number, the split data ratio and the learning rate. The result has shown that the ANN model has produced good and acceptable performance with 88.44% accuracy. For future work, the ANN model can be improved by collecting more data for its training and the performance of the model can be compared with other prediction algorithms.

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Prediction of River Water Quality Based on Artificial Neural Network