Tank Water Quality Analysis Using Machine Learning

Year : 2025 | Volume : 16 | Issue : 02 | Page : 27-34
    By

    Sanika D. Gaikwad,

  • Sakshi A. Chavan,

  • Mayuri V. Jadhav,

  • Rutuja K. Shinde,

  • A.C. Jadhav,

  1. Student, Department of Computer Engineering, Shri Chhatrapati College of Engineering, Pune, Maharashtra, India
  2. Student, Department of Computer Engineering, Shri Chhatrapati College of Engineering, Pune, Maharashtra, India
  3. Student, Department of Computer Engineering, Shri Chhatrapati College of Engineering, Pune, Maharashtra, India
  4. Student, Department of Computer Engineering, Shri Chhatrapati College of Engineering, Pune, Maharashtra, India
  5. Professor, Department of Computer Engineering, Shri Chhatrapati College of Engineering, Pune, Maharashtra, India

Abstract

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Tank Water quality is a critical factor for public health, agriculture, as well as industry. Continuous monitoring of tank water quality: temperature, humidity, water level, CO2 concentration, and pH, is vital for safe usage. Using machine learning, real-time data analysis can detect anomalies, predict issues, and optimize water management, ensuring timely responses and improved safety. This intelligent approach enhances decision-making and maintains water quality effectively in various environments.We develop an IoT-based system using ESP32/Node MCU and multiple sensors to monitor water parameters in a tank. The Python Flask web application is used to retrieve the gathered data once it has been sent to the Firebase cloud platform. A machine learning algorithm, specifically a Decision Tree Classifier (DTC) and Convolution Neural Network (CNN), is employed to predict water quality based on the gathered data and showing the quality of water. If poor water quality is detected, the system provides suggestions for precautionary measures to improve water conditions. This approach offers real-time, automated water quality analysis and can be easily expanded for large-scale applications in smart water management systems.

Keywords: Tank water quality analysis, ESP32, Node MCU, humidity sensor, DHT11, ultrasonic sensor, CO2 gas sensor, pH sensor, Firebase, Wi-Fi, Python Flask, machine learning, Decision Tree Classifier algorithm, water quality prediction, IoT sensors, data transmission, data fetching, precaution system, water monitoring, real-time analysis, Flask web

[This article belongs to Journal of Control & Instrumentation ]

How to cite this article:
Sanika D. Gaikwad, Sakshi A. Chavan, Mayuri V. Jadhav, Rutuja K. Shinde, A.C. Jadhav. Tank Water Quality Analysis Using Machine Learning. Journal of Control & Instrumentation. 2025; 16(02):27-34.
How to cite this URL:
Sanika D. Gaikwad, Sakshi A. Chavan, Mayuri V. Jadhav, Rutuja K. Shinde, A.C. Jadhav. Tank Water Quality Analysis Using Machine Learning. Journal of Control & Instrumentation. 2025; 16(02):27-34. Available from: https://journals.stmjournals.com/joci/article=2025/view=0


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Regular Issue Subscription Review Article
Volume 16
Issue 02
Received 01/04/2025
Accepted 25/04/2025
Published 16/05/2025
Publication Time 45 Days
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