Won OSECPC award for best project!

Recently, the focus of forest fire management has shifted from suppressing forest fires after they break out to fire prevention through the use of analytics and data driven decision making.

The Forestcasting tool consists of (1) a machine learning model that calculates the probability a forest fire could occur in a given area, and (2) a mathematical damage algorithm that estimates the severity of damages caused by said fire.

No existing forest fire management tool provides such a comprehensive view of both risks and damages.

Login screen
Select location using the search feature or by dropping a pin
Select any range of dates for analysis
Report: Risk score, tuneable damage score and supplementary information
Report: Location based ecozone information and historic fire data

Feature List

Fire Risk Model

Explored and compared multiple different machine learning techniques. Chose RandomForestClassifier for its performance.

Tuneable Damage Algorithm

Based on tree coverage data, vicinity score, and protected areas. Uses min-max scaling and logarithmic normalization. Managers can tune these weights within the application according to their needs.

Pin Drop Region Selection

Uses the Google Maps API to provide an intuitive way to select regions in Canada.

Date Selection Using Calendar

Uses historical weather data to provide predictions up to one year into the future.

Comprehensive Report Generation

On top of risk and damage prediction, the report provides additional data on weather, ecoregion, previous fire occurrences, nearby population, protected areas, tree coverage, elevation and more!

Data Preprocessing

Divided Canada into 20km x 20km grid locations. Then mapped weather, fire and ecoregion information to that grid. Used an alpha distribution to fill in any missing values.

Targets an Unaddressed Gap in the Market

No existing forest fire management tool provides a comprehensive view of both risks and damages.