FlashFlood Forecaster is an IoT-based system designed to predict and detect flash floods using environmental data collected by sensors. This project uses an ESP-32 microcontroller to monitor temperature, humidity, and atmospheric pressure to estimate the likelihood of flash floods and employs a rain drop sensor to detect water presence during active flooding. The system provides real-time alerts and uploads data to ThingSpeak for visualization and analysis.
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Predictive Flash Flood Monitoring:
- Collects temperature and humidity data using a DHT-11 sensor.
- Monitors atmospheric pressure using a BMP-180 sensor.
- Calculates the likelihood of a flash flood based on collected data.
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Alert System:
- LED Indicator: Activates when the flash flood likelihood exceeds 50%.
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Active Flood Detection:
- Detects water presence using a Rain Drop Sensor.
- Buzzer Alarm: Activates immediately upon water detection.
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Data Storage and Visualization:
- Uploads collected sensor data to ThingSpeak.
- Visualizes data trends for further analysis and decision-making.
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Hardware:
- ESP-32 Microcontroller
- DHT-11 Sensor for humidity and temperature
- BMP-180 Sensor for atmospheric pressure
- Rain Drop Sensor for water detection
- LED and Buzzer for Alarm Systems
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Software:
- Arduino IDE for programming
- ThingSpeak for cloud data storage and visualization
No setup is required! Simply:
- Download this repository.
- Open the "Complete_Final_Code_DHT11_V3.ino" code located in "ESP_versions" directory in the Arduino IDE, for running the latest version.
- Upload it to the ESP-32 microcontroller.
Contributions are welcome! If you'd like to improve this project or adapt it for other flood detection purposes, feel free to:
- Fork this repository.
- Make your changes.
- Submit a pull request with a clear explanation of your updates.
This project is licensed for academic and educational purposes only. You may use or modify this code for learning, research, or real-world flood detection projects. Commercial use is strictly prohibited.
If you'd like to use this project commercially, please contact the author for permission.
This project was developed as part of the Capstone at Qena STEM School by Team 23316. We would like to express our gratitude to:
- Our School Capstone Leaders and Teachers for their invaluable support and guidance throughout the project.
- Open-source communities and contributors for providing resources that made this project possible.
Special thanks to the wider STEM education network for empowering students to tackle real-world challenges with cutting-edge technology.