Title: Drinking water quality detection using genetic neural network

Authors: R. Isaac Sajan; V. Bibin Christopher; T.S. Akhila; M. Joselin Kavitha

Addresses: Department of Electronics and Communication Engineering, Ponjesly College of Engineering, Nagercoil, Tamilnadu, India ' Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur Campus, Chengalpattu Dt., Tamilnadu, India ' Department of Electronics and Communication Engineering, Mar Ephraem College of Engineering and Technology, Elavuvilai, Marthandam, Tamilnadu, India ' Department of Electronics and Communication Engineering, Marthandam College of Engineering and Technology, Marthandam, Tamilnadu, India

Abstract: Physical, chemical, and biological properties influence water quality. It assesses water treatment compliance versus standards. Most water quality standards assess ecosystem health, human safety, water pollution, and drinking water. Water quality affects supply. Microbial, chemical, and radioactive pollutants may damage drinking water. Drinking water pollution may affect babies, young children, pregnant women, the elderly, and those with impaired immune systems. Before consuming water, its purity is checked. Monitoring ensures water quality and identifies issues. Real-time ML algorithms may identify drinking water quality issues. Water quality may be checked continually and issues rectified immediately. This safeguards public health and drinking water. They may thereby improve water quality assessments. The MinMaxScaler class pre-processes data for our evolutionary neural network drinking water quality method. Also label encoding was used. The experiment yielded the best answer and 93% fitness function.

Keywords: genetic neural network; machine learning; neural networks; drinking water quality.

DOI: 10.1504/IJGW.2024.136512

International Journal of Global Warming, 2024 Vol.32 No.3, pp.267 - 281

Received: 30 Jun 2023
Accepted: 22 Jul 2023
Published online: 05 Feb 2024 *

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