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@article{177201,
        author = {Mr. Vaibhav K. Ingle and Dr. Nitin W. Ingole and Dr. Sachin V. Dharpal},
        title = {Overview on IoT Based Water Quality Monitoring},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {6710-6715},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177201},
        abstract = {Ensuring clean drinking water is essential, yet traditional monitoring methods are slow and error-prone. IoT and AI-based systems offer real-time monitoring by using sensors to measure key water quality parameters like pH, temperature, turbidity, and dissolved oxygen. Studies show that machine learning models, including ANN, SVM, and Decision Trees, improve contamination prediction. Modern systems use microcontrollers like Arduino and ESP32, along with cloud-based platforms for data visualization and alerts. Some models include LEDs for instant feedback, making them user-friendly. AI-powered decision-making further enhances water safety by enabling automated corrective measures. This review highlights recent advancements in IoT-based water quality monitoring and emphasizes cost-effective, scalable solutions. Future improvements focus on optimizing sensor networks, increasing model accuracy, and integrating smart water treatment technologies.},
        keywords = {Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Embedded Systems},
        month = {September},
        }