An IoT-Enabled LoRa-Based Intelligent Flood Detection and Early Warning System Using Machine Learning

Abstract

Floods cause extensive damage to life, property, and infrastructure, particularly in remote and rural regions where timely communication is limited. To address this challenge, this project presents an intelligent flood detection and early warning system that integrates the Internet of Things (IoT), LoRa-based communication, and machine learning technique RandomForest. The system employs multiple environmental sensors, including water-level, rainfall, soil moisture, and vibration sensors, interfaced with Arduino or ESP32 microcontrollers for real-time data acquisition. Sensor data are transmitted over long distances using low-power LoRa communication and forwarded to a cloud platform for storage and monitoring, with real-time visualization provided through an application developed using MIT App Inventor. Machine learning algorithm Random Forest analyze both historical and real-time data to predict flood risk at an early stage. During critical conditions, alerts are disseminated through a Telegram bot for public awareness and GSM-based emergency calls to authorized officials. The proposed system enhances disaster preparedness by ensuring accurate prediction, reliable communication, and timely.

Introduction

The text describes an IoT and machine learning–based real-time flood warning system designed to improve early flood detection and reduce disaster impact. Floods are becoming more frequent and severe due to climate change, and traditional monitoring methods—based on human observation or fixed thresholds—are slow, unreliable, and often fail to provide timely warnings.

To address this, the proposed system uses IoT sensors (via Arduino) to continuously collect environmental data such as rainfall, water level, humidity, temperature, vibration, and soil moisture. This real-time data is transmitted (using technologies like LoRa) to a central system for processing.

A Random Forest machine learning model is trained on labeled data to analyze these parameters and classify flood risk levels (normal, moderate, warning). The model is evaluated using metrics like accuracy, precision, and recall to ensure reliability. Once trained, it is used for real-time prediction of flood conditions.

When a potential flood is detected, the system sends instant alerts via platforms like Telegram, enabling fast emergency response. Compared to traditional systems, this approach is more accurate, scalable, cost-effective, and suitable for both rural and urban areas.

Overall, the project combines IoT sensing, machine learning, and real-time communication to create an intelligent early warning system for flood prediction and disaster management.

Conclusion

This study describes an effective and intelligent flood warning system that utilizes IoT technology with machine learning methods for monitoring and forecasting weather in real time. This new system uses sensor data like wind speed; ambient temperature; relative humidity; rainfall; water elevation; vibration and soil wetness to assess the potential for flooding; then generates warnings when needed. The Random Forest classifier employed in this new system produces both high accuracy and high reliability for all levels of flooding based on a large amount of highly complex, nonlinear data. The ability of the system to continuously monitor real-time changes in our environment increases the amount of time available to identify a potential flooding situation. Additionally, through an automated alert system using the Telegram messaging service, people can be notified immediately if a flood warning occurs allowing for prompt action to take place before the flood occurs. This drastically reduces the length of time from when the flood occurs to when it can be detected from historic methods of monitoring floods. The tests performed by other researchers have provided consistent high results regarding accuracy, precision, and recall. The new model provides both the ability to reduce false alarms while still accurately detecting critical instances of flooding. Therefore, it is believed that the new flood warning system will be a viable solution when utilized in \"real-world\" applications. As a bonus, the alert system allows people to receive notice of an impending disaster without relying solely on internet access for information.

References

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Copyright

Copyright © 2026 Dr. Y.N.D. Aravind, Potnuri Rajesh, Naga Venkata Lakshmi Taitapu, Imran Mohammad , Pankaj Kumar Shah, K.H.K. Maha Lakshmi, Sangoju Jayadeep. This 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.