The objective of this project is to build a machine learning-based forest fire prediction system that analyzes environmental factors such as temperature, humidity, wind speed, and rainfall. The system helps predict fire-prone areas and supports early preventive action for environmental protection.
Conduct background research on forest fires, their causes, environmental impacts, and current monitoring techniques used by environmental agencies.
Identify important environmental factors influencing forest fires such as temperature, humidity, wind speed, rainfall, vegetation type, and drought conditions.
Collect relevant datasets from open environmental databases or use publicly available forest fire datasets for experimentation.
Perform data preprocessing, including cleaning missing values, normalization, and transformation of environmental variables.
Conduct exploratory data analysis (EDA) to understand relationships between environmental parameters and forest fire occurrence using graphs and statistical methods.
Select appropriate machine learning algorithms such as Decision Tree, Random Forest, Logistic Regression, or Support Vector Machine for fire risk prediction.
Train and test the machine learning model using training and validation datasets.
Evaluate model performance using metrics such as accuracy, precision, recall, F1-score, and confusion matrix.
Develop a simple prediction interface or dashboard where users can input environmental conditions and receive a predicted fire risk level (low, medium, high).
Create visualization tools such as charts or heat maps to display fire risk patterns.
Compare the performance of different algorithms and identify the most accurate prediction model.
Document system architecture, dataset description, and model training procedures.
Discuss limitations of the model and suggest improvements such as real-time sensor data integration or satellite-based monitoring systems for future development.
