AI-Driven Disaster Forecasting and Response System Using Neural Networks
Objective
The objective is to build an AI-based system using neural networks to accurately predict natural disasters and provide timely alerts with response strategies, reducing risks and improving disaster management
Abstract
This project presents an AI-driven disaster forecasting and response system using Arduino, DHT11, MQ135 gas sensor, pressure sensor, rain drop sensor, LCD display, and buzzer. The sensors continuously monitor environmental conditions such as temperature, humidity, air quality, atmospheric pressure, and rainfall. The collected data is transmitted to a computer, where a CNN-based prediction model analyzes the sensor readings to forecast potential disaster situations. The monitored data and prediction results are displayed on the LCD, while the buzzer provides alerts whenever abnormal conditions are detected. The system offers a low-cost and intelligent solution for real-time environmental monitoring, disaster prediction, and early warning generation.
Keywords: Arduino, CNN, Disaster Forecasting, Environmental Monitoring, Early Warning System.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Hardware components:
- Arduino UNO
- LCD
- DHT11
- MQ135 Sensor
- Pressure Sensor
- Raindrop Sensor
- Buzzer
- connectors-10
Software requirements:
- Arduino IDE
- Embedded C
- Python
Learning Outcomes
- Arduino pin diagram and architecture
- How to install Arduino UNO / setup software
- Setting up and installation procedure for Arduino UNO
- Introduction to Arduino UNO environment / development setup Basic programming in Arduino UNO (Embedded C)
- Basics of Embedded programming using Arduino UNO
- Basics of IoT platforms
- Working of power supply
- About Project Development Life Cycle:
- Planning and Requirement Gathering (software, tools, hardware components, etc.)
- Schematic preparation
- Code development and debugging
- Hardware development and debugging
- Development of the project and output testing
- Practical exposure to:
- Hardware and software tools
- Solution providing for real-time problems
- Working with team/individual
- Work on creative ideas
- Skills developed:
- Project development skills
- Problem analyzing skills
- Problem solving skills
- Creativity and imaginative skills
- Programming skills
- Deployment
- Testing skills
- Debugging skills
- Project presentation skills
- Thesis writing skills
Paper Publishing
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
