Forest Fire Detection Using Machine Learning
Forest Fire Detection Using Machine Learning

In recent years, forest fires have become one of the most common disasters. Among its major causes, forest fires have an adverse effect on the environment as they lead to deforestation and global warming. Those responsible for preventing forest fires, collect satellite images of the forest, and if there is a fire emergency the authorities are notified. In recent years, fire accidents are rising and already have caused significant damage by the time authorities learned about them.

In addition to this, 50% of wildfires recorded is not known how they started so this needs to be monitor and stop to prevent our nature. Now, these risks of wildfires increases in extremely dry conditions, such as drought, and during high winds. This also results in very difficulties like transportation disruption, communications, power and gas services, and water supply. Needless to say a severe damage – deterioration of the air quality, and loss of property, crops, resources, animals and people. In this article, we will explain how Forest Fire Detection Using Machine Learning can help the planet.

Why do we need a forest fire detection system?

According to the reports of WHO, from 1998-2017 the wildfires and volcanic activities affected 6.2 million people with 2400 attributable deaths worldwide from suffocation, injuries, and burns.

Due to the increase in hotter, drier conditions and climate change the risk is also increasing and frequency of wildfires are growing. It is important to consider forest weather indexes along with basic weather parameters when predicting forest fires.

The problem most try to solve is a regression problem, where the forest fire damage is detected with the help of weather indexes. The frequency and intensity of forest fires or wildfires have increased over the years. Ironically, the necessity for forests and nature has become more and more significant over the years.

The world’s forest ecosystems provide a lots of value to human society. As primary habitat for a wide range of species, forests support biodiversity maintenance and conservation. Forest fires destroy this and causing a huge damage. Moreover, the disruption of the plantation, the smoke and carbon dioxide added to the atmosphere also play a vital part in harming the environment.

There are different reasons for fires to start; it can be through humans, parties, firecrackers, bonfires etc. Another reason can be natural causes, like high temperatures caused by the sun, lightning strikes, or even glass reflections.

Whether a fire is big or small, these measurements matter. Due to the fact that we must determine how significant the fire was and whether it was detected early or late.

There are a number of new ways to detect forest fires using machine learning  now and in the near future—satellite detection algorithms, machine learning algorithms, etc. To determine the extent of the damage caused by a fire, it is possible to calculate the soil damage. Many researchers have been studying forest fire detection for the last decade due to the increased number of forest fire reports from around the globe that have caused severe environmental and social damage.

Ricky Staley, an experienced firefighter, said, “Time is everything. Time is critical to saving people, animals, and the environment”. A Dryad forest fire detection system came into existence. It’s small but intelligent. The device has an inbuilt Bosch BME688 sensor that detects carbon monoxide, hydrogen, and other gases in the early stages of forest fires. In addition to analysing data on the spot, this sensor uses artificial intelligence. When it detects a fire, it immediately calls an alarm, notifying emergency services via the cloud.

Several methods have been proposed to detect forest fires, including camera-based systems, wireless sensor networks, and machine learning applications. It appears that researchers also consider environmental parameters, such as air temperature, relative humidity, barometric pressure, sound, light intensity, soil moisture, wind speed, and direction, along with other gases, to detect forest fire conditions by taking into account variations in these parameters during a fire.

Detecting fires is now easier thanks to satellite data. For example, A system developed by the Department of Geoinformatics in Stockholm, Sweden helps monitor forest fires. Such disasters can be monitored in near real-time using satellite data and machine learning techniques. In order to monitor forest fires effectively, the researchers have developed their own methods that combine a short-wave infrared index and a radar-based framework.

Furthermore, economists estimate that the Australian bushfires may have caused property damage and economic losses totalling 78-88 billion Australian dollars, making them Australia’s most expensive natural disaster. Generally, the severity of fire can be measured by tree mortality, canopy loss, or bole and crown scorch. As surrogate measures of fire intensity, these measures of fire severity are often used. For example, a particular dataset measures the coordinates of the park and the months and days when damage may occur.

Other variables come from the FWI (Fire Weather Index). The Fine Fuel Moisture Code (FFMC) represents fuel moisture of forest litter under a forest canopy. It is intended to represent moisture conditions for shaded litter fuels.. The Duff Moisture Code (DMC) represents the fuel moisture of decomposed organic material underneath the litter. The Drought Code (DC) is a numeric rating of the average moisture content of deep, compact organic layers. This code is a valuable indicator of seasonal drought effects on forest fuels and the amount of smouldering in deep duff layers and large logs. As part of the damage prediction, it also measures the relative humidity RH. The weather also plays a role, particularly the temperature, the wind and the rain


Detection of forest fire should be fast and accurate to prevent ecosystem. Wildfire may cause damage and destruction at a large scale. As we all learned many lessons from the recent cases, like Amazon forest confronted a devastating forest fire resulting in huge loss of ecosystem and adversely affecting the global conditions.

As the technology is developing, Wireless Sensor Networks (WSN) and other such systems are gaining importance to give warning and save lives. It is crucial to predict such cases, and the damage they may cause is assessed. ML and AI algorithms are increasingly effective at predicting damage and fire in the real world. There is no way to stop bushfires completely, as they are also natural disasters, but the predictable option is much easier to suppress.

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