Abstract

Forest fires pose significant environmental and economic challenges worldwide. This study leverages machine learning techniques to develop a predictive model for forest fires using the Algerian Forest Fires Cleaned Dataset. The Random Forest Classifier demonstrated exceptional predictive performance, achieving 99% accuracy, and offers a valuable tool for early forest fire detection.

Introduction

Forest fires can cause extensive ecological damage and loss of biodiversity. Accurate prediction models can assist in preventive measures and resource allocation. This research focuses on analyzing meteorological data to build a robust predictive model for forest fires.

Related work

Previous studies have explored various meteorological indices and machine learning algorithms to predict forest fires. Notable works include the use of Support Vector Machines and Neural Networks, emphasizing the importance of feature engineering and data preprocessing.

Methodology

#Dataset Overview
The dataset comprises meteorological measurements from Algeria in 2012. It includes 243 rows and 15 columns, featuring attributes such as Temperature, Relative Humidity, Wind Speed, Rainfall, and fire indices like FFMC, DMC, DC, ISI, BUI, and FWI. The target variable, ‘Classes,’ indicates fire occurrence (fire = 1, not fire = 0).
#Data Preprocessing
Missing Values: No missing values were found.
Categorical Data Encoding: The target variable was converted to numerical values.
Normalization: StandardScaler was used to normalize numerical features.

#Exploratory Data Analysis (EDA)
Correlation Matrix: Highlighted relationships between features.
Distribution Analysis: Histograms and box plots were created for numerical features to understand their distribution and identify outliers.
Time Series Analysis: Examined average temperature trends over time.
Scatter and Bar Plots: Explored feature relationships and fire occurrences by month.

#Model Selection
A Random Forest Classifier was chosen due to its robustness and ability to handle diverse feature types. The dataset was split into 70% training and 30% testing subsets.

Experiments

Initial Model Performance

Accuracy: 99%

Precision: 0.97 (class 0), 1.00 (class 1)

Recall: 1.00 (class 0), 0.98 (class 1)

F1-Score: 0.98 (class 0), 0.99 (class 1)

ROC AUC Score: 0.9886

Matthews Correlation Coefficient (MCC): 0.9719

Hyperparameter Tuning

A grid search with cross-validation optimized the Random Forest parameters. The tuned model achieved the same performance metrics as the initial model, confirming its reliability.

Results

The model demonstrated excellent predictive capabilities with consistent performance across all evaluation metrics. The high ROC AUC score and MCC underscore the model’s ability to distinguish between fire and non-fire instances.

Discussion

The results highlight the efficacy of meteorological data in predicting forest fires. While the Random Forest model performed exceptionally well, future work could explore deep learning techniques and real-time data integration to enhance predictive accuracy further.

Conclusion

This study successfully developed a highly accurate predictive model for forest fires using the Algerian Forest Fires Cleaned Dataset. The model’s performance metrics validate its potential for real-world applications in forest fire management and prevention.

References

1. Kaggle Dataset: Algerian Forest Fires Cleaned Dataset
2. Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.
3. Other relevant studies on forest fire prediction and machine learning techniques.

Acknowledgements

This project was completed as part of the CodXo Internship. Special thanks to the mentors and peers who provided guidance and support throughout this analysis.

Appendix

Code: The Python code for data preprocessing, EDA, and model training can be found here
Dataset: Available for download on kaggle