Wildfire Prediction Using Machine Learning
Data Science - Coding With Real-World Data, 2023
Scholars and forest managers have been intrigued for many years by the difficulty of predicting the incidence of wildfires. Attempts to understand the conditions that lead to the ignition and spread of wildfires can be dated at least 150 years back. It is now utmost important to predict when the wildfires will begin and how big will it be.
In this project, we are trying to predict the confidence of the forest fire based on some attributes in different cases and areas of forest fire.
As it is a regression problem, we have used,
- Linear regression
- Decision Tree
- Random forest
- Support Vector Machine
- KNN
- Adaboost
- Ridge
- Lasso
For the evaluation of the models we have used Root Mean Square Error.
Correlation Heatmap
Results
The main contributor of this project is Anika Tahsin.
You can find more about the project here.