Real-Time Flood Monitoring and Alert System Through the Integration of Wireless Sensor Network and IoT Platform using ESP32 and Thingspeak

This project presents the design and evaluation of a solar-powered, cost-efficient flood monitoring and early warning system utilizing Internet of Things (IoT) technologies for Barangay 611, Bacood, Sta. Mesa, Manila aimed at addressing the shortcomings of traditional flood detection systems such as delayed alerts, lack of real-time data, and unreliable power sources the system integrates an ESP32 microcontroller, ultrasonic sensors, GSM communication, and the ThingSpeak platform for real-time environmental data collection and visualization. The primary objective was to build a wireless sensor network capable of detecting changes in water level, analyzing the data through a decision tree algorithm, and sending SMS alerts to at-risk communities. The methodology followed a structured process involving environmental parameter identification, sensor calibration, hardware-software integration, solar energy application, and multi-phase testing in both controlled and actual flood-prone environments. A rate of change formula was applied to assess rapid fluctuations in water levels, enhancing the predictive capacity of the system. Results showed that the sensors consistently measured water levels with minimal error, and the system successfully delivered timely alerts, even during simulated power outages. The project demonstrates that by integrating wireless sensor networks with IoT platforms and renewable energy sources, this provides a reliable and scalable solution for enhancing flood preparedness and prediction. Analysis on real-time water level data was evaluated using a decision tree algorithm and rate of change calculations. This predictive capability offers substantial value to local government units and flood-prone communities by adapting to disaster response and strengthening resilience against the impacts of flooding.