Summary:
We have created a simple prediction model to predict the size of forest fires using weather and soil moisture properties. We explore a data set from northeastern Portugal that contains spatial features, temporal features, soil moisture indices, and weather features to predict the size of wildfires within the Montesinho natural park. We create a Support Vector Regression (SVR) model using the soil moisture variables, temperature, relative humidity, wind, spatial coordinates, and season. After removing outliers using Cook’s Distance method, we optimize our model using mean absolute area (MAE) and root mean square error (RMSE). Our optimized model, with C = 1.88 and γ = 0.48, produces a MAE of 8.686 and an RMSE of 28.46 on the unseen test data set, which is good for our area burned values which range from 0 to 1,090 ha.
Reviewers: Lumin Yang, Aldo de Almeida Saltao Barros , Mahsa Sarafrazi, Daniel King
Submitting Authors: Margot Vore, Hatef Rahmani, Gautham Pughazhendhi, Anahita Einolghozati
Repo Link: https://github.com/UBC-MDS/forest-fire-area-prediction-group-2 Report Link: https://github.com/UBC-MDS/forest-fire-area-prediction-group-2/blob/dev/reports/Final_report.md
Summary: We have created a simple prediction model to predict the size of forest fires using weather and soil moisture properties. We explore a data set from northeastern Portugal that contains spatial features, temporal features, soil moisture indices, and weather features to predict the size of wildfires within the Montesinho natural park. We create a Support Vector Regression (SVR) model using the soil moisture variables, temperature, relative humidity, wind, spatial coordinates, and season. After removing outliers using Cook’s Distance method, we optimize our model using mean absolute area (MAE) and root mean square error (RMSE). Our optimized model, with C = 1.88 and γ = 0.48, produces a MAE of 8.686 and an RMSE of 28.46 on the unseen test data set, which is good for our area burned values which range from 0 to 1,090 ha.
Reviewers: Lumin Yang, Aldo de Almeida Saltao Barros , Mahsa Sarafrazi, Daniel King