Published Paper Details:

This project addresses critical water quality issues by developing a system for real-time waterquality monitoring and ground-level water analysis. Traditional water monitoring approacheshave limitations, such as delayed data collection and limited data accuracy, which impedetimely pollution detection and response. This study proposes an Internet of Things (IoT)-based solution to bridge this gap, focusing on creating a sensor network that enables continuous monitoring of water quality in industrial and domestic water sources. Utilizing devices like the Arduino Uno, ESP8266, and various sensors--including those for temperature, pH, turbidity,Total Dissolved Solids (TDS), and conductivity--the system gathers data on essential waterquality parameters. This data is then processed and visualized using the ThingSpeak platform, allowing real-time updates accessible to stakeholders.The system's core methodology includes sensor data acquisition, preprocessing, and analysis via machine learning models to assess water quality trends and predict pollution risks. With a focus on adaptability, the system supports varied environmental contexts, enhancing its utilityacross different water bodies. This research contributes significantly to sustainable water management, especially in pollution-prone industrial zones, by offering a scalable, real-time solution for continuous water quality assessment. The implementation of this system has the potential to inform and improve water resource policies, reinforcing environmental and publichealth protection.