How AI is Reducing the Devastating Effects of Wildfires Across the Globe

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Climate change has intensified wildfires, causing significant damage to communities, loss of life, and immense pressure on fire fighting resources. To combat this crisis, governments are swiftly adopting advanced AI technology. Fire-fighters and start-ups are employing AI-powered cameras to detect smoke early and AI models to predict potential wildfire ignition points.

Wildfires affect many countries through human and animal deaths and the loss of millions of hectares of forests.

A recent report by the United Nations Environment Programme (UNEP) predicts the growth of wildfires by 50% by 2100.

According to the authors of a report on wildfire prevention and management, Arunima Sarkar, Sirin AltiokŞebnem, and Güneş Söyler, “The escalating number of wildfires worldwide renders it necessary to develop innovative solutions and novel technologies.”

This is where artificial intelligence (AI) comes in, and it can redefine how we manage and mitigate wildfire threats.

Authorities use various ways to mitigate wildfires such as data from multiple sources, deploying manpower and machines, and tracking wildfires across vulnerable regions. Still, the methods, technologies, and systems must be integrated to develop a framework.

This article discusses how to develop a framework and its benefits.

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How AI Can Bring an Integrated Approach to Wildfire Prevention

Government agencies use various tools and systems to manage wildfires. For example, tracking social media for alerts or discussions on wildfires in a region, predictive algorithms that predict the degrees of possibilities of wildfire breakouts, resource management in deploying manpower, and tracking wildfire management performances.

This is not to say that the tools and systems are ineffective, but there is a scope for significant improvement. It’s possible that the systems, especially computer systems and solutions, aren’t talking to each other or integrated.

For example, predictive algorithms may flag a forest area as highly vulnerable and recommend immediate evacuation of humans and animals. However, alerting the agencies to act may be bureaucratic or manual, delaying action.

How about integrating two different systems – predictive systems and wildfire manpower management systems? When the predictive system displays the orange code to start evacuating, it integrates with the manpower system. Immediate notifications are sent to the mobile devices of people responsible for evacuating the people and animals.

Single Source of Truth

When you want to mitigate a threat as dangerous as a wildfire that could cost you lives and destroy forests and properties, the last thing you want is information from multiple unverified sources. A framework with well-defined and validated sources of information from which data flows into the system for further processing can speed up warnings.

An integrated framework defines the types of resources needed, roles, time of action, estimated time of arrival, and the workflows and process flows that specify how data or information will flow to the right resource at the right time. It will also define a hierarchy of resources so that emergencies are better handled.

How Does AI Help Manage Wildfires?

Let us take the example of how Google’s products enable authorities to manage wildfires. In its blog, Google claims that in 2023, it has provided timely safety information to over 30 million people across 120 wildfire events worldwide.

Its Wildfire Boundary Tracker uses AI and satellite imagery to provide real-time information on wildfires, especially in countries like the US, Mexico, Canada, and Australia.

The systems take real-time data from the National Oceanic and Atmospheric Administration’s (NOAA), Geostationary Operational Environmental Satellites (GOES), and Google Earth’s data analysis capabilities and analyze various incidents of wildfires around the Earth.

The system runs various computations on this data in the Google Earth Engine to identify the affected areas with a lot of accuracy.

Google has partnered with the U.S. Forest Service to help them train firefighters to mitigate wildfires more effectively. This helps bring the correct information on the location and the size of the wildfire and have the proper training and types of equipment.

The U.S. Forest Service has a model for training the firefighters. Still, the model leverages machine learning (ML) to plan effective fuel treatments and fight widespread wildfires more safely and effectively.

The model takes a lot of data from different sources, such as satellite imagery. The National Oceanic and Atmospheric Administration’s (NOAA) and Geostationary Operational Environmental Satellites (GOES) and process the data to derive data such as potential hotspots, current weather, history of the area in terms of wildfires and then offer predictions.

The Bottom Line

AI is already proving itself a strong ally in the fight against wildfires, and it can effectively complement the efforts of agencies, fire services, and firefighters.

The main benefits are predictive analysis, timely alerts, SOS, real-time analysis, and the capability to analyze vast amounts of data. The ability to analyze massive volumes of data is especially pertinent given the growing incidences of wildfires worldwide.

It’s important to note that because of global warming, wildfires have been on the rise. However, no matter how many advances AI makes, an integrated approach or framework must be adopted to capitalize on the benefits.

While progress is being made, an integrated framework must be implemented at the global, national, and regional levels.

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Kaushik Pal
Technology writer
Kaushik Pal
Technology writer

Kaushik is a technical architect and software consultant with over 23 years of experience in software analysis, development, architecture, design, testing and training. He has an interest in new technologies and areas of innovation. He focuses on web architecture, web technologies, Java/J2EE, open source software, WebRTC, big data and semantic technologies. He has demonstrated expertise in requirements analysis, architectural design and implementation, technical use cases and software development. His experience has covered various industries such as insurance, banking, airlines, shipping, document management and product development, etc. He has worked on a wide range of technologies ranging from large scale (IBM…