ABSTRACT:
This project presents the development of a water quality monitoring system using ML and IOT integrated with machine learning (ML) to ensure real-time water analysis, prediction, and alerting. The system employs a range of environmental sensors to continuously monitor vital water quality parameters such as pH, temperature, turbidity, and contamination levels. These sensor readings are transmitted via IoT technology to a cloud-based platform for real-time storage, visualization, and further processing.
To enhance decision-making capabilities, machine learning algorithms are used to analyze the collected data, classify the quality of water (e.g., Safe, Contaminated, Highly Polluted), and predict potential water quality issues before they become critical. This predictive mechanism is essential for timely intervention in agricultural, industrial, and residential water management applications.
The integration of IoT allows for remote monitoring, automation, reducing manual testing and enabling scalable, real-time deployment. The proposed system offers a cost-effective, intelligent, and environmentally sustainable solution for water quality management, contributing to public health, efficient water use, and environmental protection.
INTRODUCTION:
Water quality is a crucial factor influencing human health, agriculture, aquatic ecosystems, and industrial operations. Contaminated water can lead to serious health hazards, environmental degradation, and economic losses. Traditional water quality assessment methods, which rely on manual sampling and laboratory analysis, are often time-consuming, expensive, and lack the ability to provide real-time insights.
To overcome these limitations, this project proposes a smart Water Quality Monitoring System powered by the Internet of Things (IoT) and Machine Learning (ML). The system utilizes a network of IoT-enabled sensors to continuously monitor key water quality parameters such as pH, turbidity, dissolved oxygen, temperature, and electrical conductivity. These sensors are connected to a microcontroller (Arduino) which collects and transmits the data to a cloud or web-based platform for further analysis.
Machine learning algorithms are then applied to the real-time data to detect anomalies, classify water quality, predict contamination trends, and generate actionable insights. This predictive capability enables early warnings and supports proactive decision-making for water management.
The integration of IoT and ML significantly enhances the system’s efficiency and adaptability, making it suitable for diverse applications such as drinking water supply monitoring, aquaculture systems, agricultural irrigation, industrial wastewater control, and environmental conservation. By leveraging modern technologies, this smart monitoring system contributes to improved water safety, resource sustainability, and reduced ecological and health risks.

Objective
The goal of this project is to create a smart system that checks water quality using IoT and Machine Learning. It will:
• Measure important water values like pH, turbidity, temperature, and more.
• Use Machine Learning to find problems and predict future issues.
• Show the data on a website so users can see it from anywhere.
• Send alerts if the water becomes unsafe.