Forest fire prediction

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  ANN accuracy can reach up to 80% or higher，it is the

Inspiration

In the past few years, forest fires in Australia have had a significant impact on the global environment. However, in recent years, due to global climate issues, frequent forest fires have made prevention and control of forest fires increasingly important for protecting the environment

What it does

Explore the impact of four meteorological factors and four fire size judgment factors on forest fires in the dataset. After the model is successfully trained, we can input data from anywhere for prediction and determine which factor is affecting the fire. Corresponding measures will be taken to prevent and control it, improving the efficiency of forest protection

How we built it

Use multiple machine learning models for prediction and test accuracy to find the most suitable model

Challenges we ran into

How to perform operations in data preprocessing to make the results more accurate

Accomplishments that we're proud of

The highest model accuracy reaches over 80%

What we learned

Improved design of data preprocessing and application of machine learning models

What's next for Forest fire prediction

On the basis of the successful model, we can try to add more influencing factors to make the model more complex and accurate

Built With

• machine-learning
• python