AI Case Study

Monta Vista High School students build a model to predict wildfires using Google Tensorflow

Two high school students at Monta Vista High School are using machine learning libraries in Tensorflow to estimate moisture content and size of biomass to predict wildfire more accurately than just using factors such as wind speed, humidity, temperature etc.

Industry

Public And Social Sector

Education And Academia

Project Overview

"Available tools measure most of the factors responsible for wildfires like wind speed, wind direction, humidity, temperature. However, biomass, which is created by years of falling branches and trees, is challenging to estimate and measure. Using TensorFlow, Google’s open source machine learning tool, images of biomass can be anlysed and their moisture content and size can be estimated to determine the amount of dead fuel.

Used in a network of sensors, our Smart Wildfire Sensor device could remove the need for fire prevention crews to physically visit forest areas to collect samples of dead fuels and classify them manually. It will also be able to predict the likelihood of wildfire in a forest at a 100 square meter granularity level. Perfect estimation of biomass buildup could significantly impede the speed and ferocity with which these fires spread, reducing costs to fight them and protecting homes and lives."

Reported Results

Research; Results not yet available

Technology

Function

Strategy

Analytics

Background

Current tools measure several factors such as wind, humidity, temperature but does not take into account biomass.

Benefits

Data

"Images of biomass can be anlysed to estimate moisture content and size"