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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64753
Volume:
10 |
6 |
Jun 2026
Pages:
1-7
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Computer Science
Rajiv Gandhi College of Engineering and Technology
This project aims to develop a real-time water quality monitoring system using an Arduino Uno microcontroller in conjunction with a Total Dissolved Solids (TDS) meter and a pH meter. The goal is to provide an efficient and affordable solution to continuously monitor the quality of water sources, enabling quick detection of potential contamination. By integrating these sensors, the system will measure key parameters such as water's pH level and the dissolved solids, which are critical indicators of water quality.The TDS meter will measure the concentration of dissolved salts, minerals, and other impurities in the water, giving an indication of the overall purity and health of the water. The pH meter will measure the acidity or alkalinity of the water, which can be affected by various environmental factors and pollutants. The Arduino Uno will process the data from these sensors and output the values via an Android application, which will allow users to monitor the real-time quality of the water.In addition to the display, the system will be capable of sending alerts when the water quality falls below acceptable thresholds, ensuring immediate action can be taken. This low-cost, easily deployable system can be used in diverse applications, including in households, industrial facilities, aquariums, or agricultural setups, where maintaining water quality is essential.By providing an easy-to-use interface and immediate data feedback, the system can assist in maintaining safe water quality standards and contributing to public health and safety.
KEYWORDS
AquaMo, an innovative AI-powered water quality analysis and microorganism prediction system, has been successfully developed to provide real-time monitoring and alerts for ensuring safe drinking water. AquaMo has the potential to revolutionize the way we monitor and manage water quality. By leveraging advanced machine learning algorithms and real-time data collection, AquaMo can provide accurate predictions of water quality and detect microorganisms with high precision. By harnessing the power of AI and Deep learning, AquaMo has the potential to make a significant impact on water quality monitoring and management, ultimately contributing to a healthier and more sustainable environment.
[1] X. Gao and Y. Di, “Internet of things data preprocessing method for prediction in aquaculture,” Jiangsu Agricultural Science., 2018, Vol. 46, pp. 187- 192. [2] L. Xia and Y. Zhu, “Research on Approach Optimization of Missing Water- Quality Data,” Water Resources and Power., 2018, Vol. 36, pp. 158-161+85. [3] J. Wu, “Application of chaos and fractal models to water quality time series prediction,” Environmental Modelling & Software., 2009, Vol. 24, pp. 632-636. [4] Y. Yue and T. Li, “The Application of a Fuzzy-Set Theory Based Markov Model in the Quantitative Prediction of Water Quality,” Journal of Basic Science and Engineering., 2011, Vol. 19, pp. 231-242. [5] Z. Li, Y. Jiang and J. Yue, “An Improved Gray Model For Aquaculture Water Quality Prediction,” Intelligent Automation & Soft Computing., 2012, Vol. 18, pp. 557-567. [6] G. Tan, J. Yan and C. Gao, “Prediction of water quality time series data based on least squares support vector machine,” Procedia Engineering., 2012, Vol. 31, pp. 1194-1199. [7] S. Liu and L. Xu, “Prediction of dissolved oxygen content in river crab culture based on least squares support vector regression optimized by improved particle swarm optimization,” Computers & Electronics in Agriculture., 2013, Vol. 95, pp. 82-91. [8] S. Liu, L. Xu and D. Li, “Multi-scale prediction of water temperature using empirical mode decomposition with back-propagation neural networks,” Computers & Electrical Engineering., 2016, Vol. 49, pp. 1-8. [9] H. Han, Q. Chen and J. Qiao, “An efficient self-organizing RBF neural network for water quality prediction,” Neural Networks the Official Journal of the International Neural Network Society., 2011, Vol. 24, pp. 717-725. [10] X. Miao, T. Ge and H. Gao, “A prediction model for dissolved oxygen level in a fish pond based on combination of neural network and genetic algorithm,” Journal of Dalian Ocean University., 2011, Vol. 26, pp. 264-267.
Publication Date
6/10/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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