AI-Based Water Quality Monitoring and Predictive Analytics System

Author

Kiran Kumar KM, G Brahmani, Anusha H


Abstract

 Water quality is an important factor in protecting public health, environmental sustainability, and aquatic ecosystems. Monitoring water quality manually can be time-consuming and challenging, particularly when dealing with large volumes of data collected from different locations and sources. Traditional monitoring approaches may also make it difficult to identify abnormal changes, analyze historical patterns, and predict potential water quality problems.

This paper proposes an AI-Based Water Quality Monitoring and Predictive Analytics System that provides a digital platform for recording, monitoring, analyzing, and predicting water quality conditions. The proposed system considers important water quality parameters such as pH, turbidity, temperature, dissolved oxygen, and other available indicators. The data can be entered manually, imported from laboratory reports, or received from compatible IoT sensors and external datasets.

The system uses Python-based data processing and machine learning techniques to analyze historical and current observations. It provides interactive dashboards, graphical representations, threshold-based alerts, anomaly detection, and predictive analysis. Additional features include sample management, location-based monitoring, report generation, historical comparisons, and an AI-based assistant for understanding water quality information.

The proposed application aims to improve data management, support early identification of unusual water quality patterns, and provide useful analytical information for decision-making. It is intended as a software-based monitoring and decision-support platform and does not replace laboratory testing or certified water quality assessments.


Keywords

Artificial Intelligence, Water Quality Monitoring, Machine Learning, Predictive Analytics, Anomaly Detection, Data Visualization, Environmental Monitoring.



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References


The following sources can be consulted and cited after verifying the exact documents and editions used in the project:

  1. World Health Organization (WHO), Guidelines for Drinking-water Quality, Fourth Edition Incorporating the First and Second Addenda, 2022.
  2. Central Pollution Control Board (CPCB), Government of India, water quality monitoring publications and guidelines.
  3. S. Environmental Protection Agency (EPA), water quality monitoring and assessment resources.
  4. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, Vol. 12, pp. 2825–2830, 2011.
  5. McKinney, “Data Structures for Statistical Computing in Python,” Proceedings of the 9th Python in Science Conference, 2010.
  6. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016.
  7. Relevant research papers on machine learning applications in water quality prediction, anomaly detection, and environmental monitoring.

Note: Add the exact URLs, publication details, access dates, and any additional research papers actually consulted before submitting the final paper.

 

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