IoT-Based Smart Flood Monitoring and Early Warning System
Nirmalkumar S. Benni, Sathish Kumar S, Arun Kumar G, Veeramma Yatnalli, Saroja S Bhusare, Manjunath G. AsutiIntroduction:
Floods pose a significant hazard in India, causing severe damage to life, property, and the economy. Existing flood monitoring systems often suffer from delayed response, limited coverage, and high costs. The objective of this study is to design and implement a low-cost, real-time IoT-based smart flood monitoring and early warning system that can improve prediction accuracy and provide timely alerts to minimize flood impacts
Methods:
The proposed system integrates multiple sensors-ultrasonic for water level, water flow sensors, and DHT22 for temperature and humidity-with an Arduino Uno microcontroller. Data is transmitted to the ThingSpeak cloud platform using the ESP8266 Wi-Fi module and visualized via the ThingView mobile application. A GSM module sends SMS alerts to authorities and residents when threshold conditions are detected. The system was simulated using Proteus Professional to verify performance, and individual modules were tested for accuracy and responsiveness.
Discussion:
The proposed system overcomes limitations of traditional flood monitoring approaches by enabling automated, continuous, and low-cost sensing with cloud-based data access. Multisensor integration reduces false alarms compared to single-parameter systems. While the Wi-Fi + GSM approach provides effective coverage for urban and semi-urban areas, rural deployments may require extended-range communication protocols. Security enhancements and machine learning integration are recommended for predictive analytics and robust performance in div
Conclusion:
This IoT-based flood monitoring and early warning system provides a scalable, affordable, and effective solution for real-time flood risk management. By integrating multiple environmental parameters, cloud storage, and multi-channel alerts, it significantly improves upon existing methods. The architecture offers a strong foundation for future enhancements, including AIdriven prediction models and secure data transmission protocols, to further strengthen disaster preparedness and response.