Ready. Aim. Fire.

Project Info

 Sunshine Coast Crew thumbnail

Team Members


Daniel

Project Description


Using citizen science to help prepare for, predict and prevent losses from bushfire events

Current fire detection systems are limited to public observation and reporting. This is a low cost, low fidelity solution to a potentially catastrophic problem. Every minute of delay in reporting and responding to a bushfire results in a logarithmic escalation of the required time and resources needed to bring the situation under control.

A data driven system could despatch a helicopter within minutes, carrying a tonne of water that would douse the flame and nullify the situation in under 30 minutes, de-risking the situation before it gets out-of-control.

At present we use data models only to predict the likelihood of fire on any given day; we are all familiar with the words “Total Fire Ban”. But what do we know about the consequence of a fire starting? Likelihood and consequence need to be better related, and data can help with that.

Image Credit: https://en.wikipedia.org/wiki/2019%E2%80%9320_Australian_bushfire_season#/media/File:Gospers_Mountain_Fire.jpg


#bushfire #citizen science #data science #data modelling #deep learning

Data Story


We have data available, both live and historic, on temperature , humidity, wind, rainfall, river levels, soil composition, soil moisture, vegetation/crop composition, vegetation/crop moisture, air purity, airborne particulate matter, mapped fire danger indices, and lastly, satellite and camera imagery.

Surely a data model armed with all this data could not just see a fire coming from a mile away, but predict with accuracy what environmental conditions indicate a fire looming.


Evidence of Work

Video

Homepage

High-Res Image

Team DataSets

Fire and other related incident feed

Description of Use Example of input path to use for deep learning to train an AI on incidents and environmental conditions at the time

Data Set

Environment Monitoring API

Description of Use Offical readings of air quality particulate matter, testing for smoke during bushfires

Data Set

Decadal Forest Fire Danger Index (2006-2096)

Description of Use Sampled images and overlays of fire danger index in non-inhabited areas. Very large file set so only sampled

Data Set

Australian Biomass for Bioenergy Assessment - Queensland data

Description of Use Estimation of economic loss of flammable material in the event of bushfire

Data Set

Challenges

Bushfire Ready

What can we do to prevent or prepare for bushfires?

Go to Challenge | 12 teams have entered this challenge.

Understanding the impact of climate change on extreme weather events on the Sunshine Coast

From bushfires to rain and flooding, weather events are increasing in number and strength due to climate change. How might we provide policy advisors and researchers with the keys to communicate the increasing risks and drive change in government policy and public behaviour?

Go to Challenge | 8 teams have entered this challenge.

Citizen Science

How might we create a citizen science experiment to support a better understanding of what is happening in the State of Victoria?

Go to Challenge | 12 teams have entered this challenge.

Data-driven decisions for improved disaster planning, management or recovery

How might we equip decision makers with a data driven tool to support their communities in the face of climate change and natural disasters.

Go to Challenge | 15 teams have entered this challenge.

Engaging communities in hazard reporting & safety

How might we better prepare & deal with natural disasters in Australia?

Go to Challenge | 18 teams have entered this challenge.