1. FOREST FIRES ANALYSIS AND PREDICTION SYSTEM
Advisor: Dr. Jerry Gao
Team Members:
- Abhishek Andhare
- Ankit Gupta
- Janish Siroya
- Manjeet Singh Brar
- Riona Maria Mascarenhas

2. Agenda Abstract Introduction/Background Project architecture
System components
Technologies used
Methodology
Results and analysis
Conclusion

3. Introduction
Wildfires are one of the biggest catastrophes faced by our society today causing irrevocable damages.
These forest fires can be man-made or caused by mother nature by different weather conditions, torrential winds.
These fires cause damages not only to the environment they also destroy vast homes and property.
Hence we devised a panacea “Fire Analysis and Prediction System” to fight this disaster and help our planet.

4. Abstract
Wildfires are an uncontrollable disaster which cause damages to the society as well as endanger the nature.
Forest Fire Analysis and Prediction System is built to detect the forest fires and then performs prediction of the fire spread.
With the help of big data analysis and machine learning algorithms we intend to build a tool which serves an aid to the society and thus prevent the occurrences of this disaster.

5. System Architecture

6. Technologies Used Python OpenCV MongoDB MEAN Stack CSS HTML5
Selenium for testing

7. Implementation and Methodology

8. Detection of Forest Fires
HoG Algorithm and SVM Classification:
Collect the forest fire image datasets from the satellite.
The HoG feature descriptor uses the sliding detection window which is 64x128 pixels wide slides through the image.
The detection process is based on the support vector machines.
The SVM classifier acts upon the given test data and enables the detection of fires.

9. Forest Fire Detection Model

10. Prediction of Fires
Implemented Rothermel’s model for the prediction of fire and the fire spread.
Various input parameters – temperature, pressure, slope, fuel moisture considered.
Prediction of rate of spread of fire, intensity of fires, flame length, the direction of spread is calculated.

11. Forest Fires Prediction Model

12. Hybrid Model Of System

13. Results and Analysis

14. Conclusion
Through this project developed a tool to help mitigate and prevent the losses seen due to forest fires.
Innovative solution which detects the forest fires and then performs the prediction based on the prevalent conditions.
Learnt to build a hybrid model using big data analysis and machine learning techniques.

15. Future Scope
Integrate live satellite data and process real time processing of the fires .
Enhance the time complexity of the detection of fires to improve the speed.

16. THANK YOU 