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Forest Fire Monitoring and Alert System Documentation

Welcome to the documentation for the Forest Fire Monitoring and Alert System. This system combines satellite-derived environmental data, machine learning (ML) predictions, and Internet of Things (IoT) sensors to provide real-time forest fire monitoring, risk assessment, and immediate alert notifications.

Table of Contents

• Key Features
  • Environmental Data Processing
  • IoT Sensors Implementation
  • Alert Notification System
• Environmental Data Processing
  • Input Data
  • Machine Learning Model
• IoT Sensors Implementation
  • Sensor Deployment
  • Data Collection
• Alert Notification System
  • Immediate Alerts
  • Notification Channels
• Sequence Flow: User Engagement and Alert Notification
  • Step 1: App/Website Access
  • Step 2: Initial Location Access
  • Step 3: Home Screen
  • Step 4: Fire-Prone Areas Overview
  • Step 5: Live Fire Locations
  • Step 6: User Location Tracking
  • Step 7: Proximity Alert
  • Additional Features (Side Features)
• Conclusion

Key Features

Environmental Data Processing

• Input Data: Satellite-derived data including oxygen levels, humidity, and temperature.
• Machine Learning Model: Predicts the likelihood of a forest fire based on the environmental variables.

IoT Sensors Implementation

• Sensor Deployment: Install IoT sensors strategically in fire-prone areas.
• Data Collection: Sensors continuously monitor on-ground conditions.

Alert Notification System

• Immediate Alerts: If the ML model predicts a high risk of fire or if an actual fire is detected by sensors, immediate alerts are sent.
• Notification Channels: Alerts are sent to nearby residents via mobile apps, SMS, and other communication channels.

Environmental Data Processing

Input Data

The system utilizes satellite-derived environmental data, including oxygen levels, humidity, and temperature. This data serves as input for the ML model.

Machine Learning Model

A robust ML model analyzes the environmental data to predict the likelihood of a forest fire. This model is trained on historical data to enhance predictive accuracy.

IoT Sensors Implementation

Sensor Deployment

IoT sensors are strategically deployed in areas identified as high-risk based on the ML predictions. These sensors continuously monitor on-ground conditions.

Data Collection

The sensors collect real-time data on environmental variables such as temperature, humidity, and oxygen levels. In case of a significant change, the sensors trigger alerts.

Alert Notification System

Immediate Alerts

1. ML Prediction Alerts: If the ML model predicts a high likelihood of a forest fire based on satellite data, immediate alerts are generated.
2. Sensor Detection Alerts: When on-ground sensors detect an actual fire, immediate alerts are triggered.

Notification Channels

Residents in the affected areas receive alerts through various channels:

• Mobile Apps: A dedicated mobile app provides real-time alerts.

Sequence Flow: User Engagement and Alert Notification

Step 1: App/Website Access

1. User opens the Forest Fire Monitoring App or visits the website.
   • No login is required for basic functionalities.

Step 2: Initial Location Access

2. First-time Location Access:
   • If it's the user's first time accessing the app, the system requests permission to access the device's location.
   • Upon user approval, the app captures the user's location coordinates.

Step 3: Home Screen

3. Home Screen:
   • The user is directed to the home screen displaying a map interface.

Step 4: Fire-Prone Areas Overview

4. View Fire-Prone Areas:
   • The map highlights areas prone to fire based on satellite data and ML predictions.
   • Users can explore fire-prone regions without additional features.

Step 5: Live Fire Locations

5. Live Fire Display:
   • The map also shows live fire locations in real-time, if available.

Step 6: User Location Tracking

6. Continuous Location Tracking:
   • The app continuously tracks the user's location in the background.

Step 7: Proximity Alert

7. Proximity Alert:
   • If the user's location is within a specified proximity to a live fire (configurable distance), an immediate alert is triggered.

Additional Features (Side Features):

• Custom Alerts:
  • Users can set custom alert preferences based on different fire risk levels.
• Educational Resources:
  • Access to educational resources on fire prevention, safety measures, and evacuation plans.
• Emergency Contacts:
  • Emergency contact information is readily available.
• Crowdfunding for NGOs:
  • Users can contribute to NGOs involved in forest fire prevention and control.

Conclusion

The Forest Fire Monitoring and Alert System seamlessly integrate satellite-derived data, machine learning, and IoT sensors to enhance forest fire prevention and response efforts. By providing accurate predictions and real-time alerts, the system contributes to early detection, minimizing the impact of forest fires on communities and the environment.

link of ppt

https://www.canva.com/design/DAF6ZS7kMQU/Z5aoHCE3R5x91LY5TfpxDw/edit?utm_content=DAF6ZS7kMQU&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton