Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{172148,
        author = {Prajakta Ramdas Borhade and Prof. Shrishail S. Patil and Prof. Nitin M. Shivale and Samrudhi Ramdas Biradawade and Anagha Mansing Patil and Vaishnavi Mahesh Matre},
        title = {IOT-BASED FLOOD PREDICTION AND WARNING SYSTEM USING DAM DATA MONITORING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {2439-2445},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=172148},
        abstract = {The project aims to provide a cost-effective and technically feasible solution for early flood detection and disaster prevention. By leveraging IoT sensors such as water level, humidity, and temperature sensors, combined with microcontrollers (Arduino) and communication technologies like GSM and LoRaWAN, the system monitors real-time dam and environmental data. The data is transmitted to a cloud platform, where machine learning algorithms process it to predict potential flood risks. Alerts are then sent to local authorities and the public, enabling timely action.
The technical feasibility of the project is ensured by using widely available hardware and software, which are scalable to monitor multiple dams. Economically, the system is built with low-cost components, and operational expenses are minimized through cloud infrastructure and energy-efficient IoT devices. Financially, the project offers a high return on investment (ROI) by reducing flood-related damage and potentially qualifying for government grants and disaster-prevention funding. The implemented system provides accurate real-time flood prediction with high reliability, due to the integration of IoT and machine learning technologies. The system successfully alerts local populations and authorities, reducing flood impacts. With minimal maintenance costs and low initial investment, the project proves to be financially viable, scalable, and effective in preventing flood disasters.},
        keywords = {},
        month = {January},
        }