Quality index metrics with Bi-GRU-based water quality prediction

ABSTRACT

The precise assessment of groundwater resources is significant in the face of growing water demands, environmental pollution, and degradation. However, traditional methods of water quality prediction are inadequate in dealing with large volumes of data or missing data. Machine learning-based prediction methods are being explored to address this issue. Currently, single-indicator techniques are commonly used, but they may not accurately forecast multimodal water quality or capture inter-indicator connections. In this study, a Bi-GRU is proposed to predicting water quality based on quality index data. To evaluate the performance of our proposed method, we utilize Kaggle Datasets, which provide a diverse range of water quality data. We compare our results with existing methods such as MLP, LSTM, and GRU, using metrics including accuracy, precision, recall, and f-score. Overall, our study demonstrates that the Bi-GRU model is highly effective in predicting water quality based on quality index data. The results of our experiments indicate that our proposed method surpasses traditional approaches like MLP, LSTM, and GRU in terms of accuracy, precision, recall, and f-score. These findings have significant implications for improving water quality monitoring and pollution prevention efforts, enabling better management of groundwater resources in the face of growing water demands and environmental challenges.

KEYWORDS:

• Water quality prediction
• machine learning techniques
• deep learning techniques
• and input parameters

Nomenclature

Bi-GRU	=	Bi-Gated Recurrent Unit
KNN	=	K-Nearest Neighbor
TN	=	Total Nitrogen
ANN	=	Artificial Neural Network
LSTM	=	Long-Short Term Memory
GCN	=	Graph convolutional Network

Acknowledgements

There is no acknowledgment involved in this work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Authorship contributions

All authors are contributed equally to this work

Data availability statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study

Ethics approval and consent to participate

No participation of humans takes place in this implementation process

Human and animal rights

No violation of Human and Animal Rights is involved.

Additional information

Notes on contributors

K. Mohan Raj

K. Mohan Raj is a seasoned engineer and academician with a solid educational foundation in Electronics and Instrumentation Engineering. He earned his Bachelor's degree from SRM Valliammai Engineering College, where he developed a strong understanding of electronic systems and instrumentation principles. Building upon his undergraduate studies, Mohanraj pursued a Master's degree in Control and Instrumentation at Karunya University, delving deeper into the intricacies of controlling systems and instrumentation, enhancing his expertise in precision and control mechanisms. Currently serving as an Assistant Professor at Sri Sairam Engineering College in Chennai, Mohanraj brings a wealth of knowledge and practical experience to his students. His passion for teaching and sharing his expertise has made him a respected figure among his peers and students alike. He is dedicated to nurturing the next generation of engineers, fostering their understanding of complex engineering concepts and preparing them for the challenges of the industry.

K.S. Vairavel

Dr. K.S. Vairavel received his B.E. (Electronics and Instrumentation Engineering) Degree from Bharathidasan University, Tiruchirappalli in April 2001, M.E. (Applied Electronics) Degree from Anna University, Chennai in June 2007 and Ph.D. (Information and Communication Engineering) Degree from Anna University, Chennai in October 2018. He is presently working as an Associate Professor & Head, Department of Electronics and Instrumentation Engineering, Bannari Amman Institute of Technology, Sathyamangalam. He is having 21 years of teaching experience in various engineering colleges. His research interest includes Biometrics, Image Processing, Control Systems and Process Control. He is the life member in ISTE, IET and annual member in ACDOS. He has published 26 papers in International and National Journals, 28 papers in International and National Conferences.