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@article{186698,
        author = {Mr. Ayan Shaikh and Mr. Om Gaikwad and Mr. Hariprasad Bhagat and Ms. Tejashri Mandlik and Mr. Sahil Bhalerao},
        title = {Survey On AI-Powered IoT System for Early Detection and Forecasting of Urban Water Contamination},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {2572-2577},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186698},
        abstract = {Urban water contamination is an escalating problem driven by industrial waste, sewage discharge, and chemical pollutants, posing severe risks to human health and ecosystems. This paper presents a comprehensive survey on an AI-powered IoT system for the early detection and forecasting of urban water contamination. The proposed framework integrates IoT-enabled sensors—including pH, turbidity, Total Dissolved Solids (TDS), and dissolved oxygen sensors—to collect real-time water quality data. The gathered data is processed on a cloud-based platform, where Artificial Intelligence (AI) techniques, particularly K-Nearest Neighbors (KNN), are applied to identify contamination levels and predict future water quality trends. By enabling continuous monitoring and intelligent prediction, the system aims to support early intervention, efficient decision- making, and sustainable urban water management. This study emphasizes how the integration of IoT, AI, and cloud computing can revolutionize environmental monitoring and help achieve cleaner, safer, and smarter urban water systems.},
        keywords = {IoT, Artificial Intelligence, Water Contamination, Smart Monitoring, Arduino Uno, ESP32, Firebase, KNN Algorithm, Cloud Computing, Real-Time Data, Predictive Analytics, Urban Water Quality},
        month = {November},
        }