Forests play a crucial role in both ecological and economic value for a country. Wildfires are one of the major factors that ravage forests and destroy their ecosystems. Not only do they destroy valuable natural resources, but they also endanger millions of lives. Each year, wildfires consume around 400 Mha of forest cover worldwide. Alarmingly, 90% of these fires are caused by human activity, with the remaining 10% being attributed to natural causes. The recent increase in climate change has greatly increased the number of fire incidents every year. The occurrence of forest fires is a complex process that depends on many interconnected factors such as the source of ignition, fuel composition, weather, and topography. Traditional methods of wildfire prediction heavily rely on empirical and statistical methods developed using these factors. However, due to inadequate data and non-linear relationships between the factors, fire prediction models are often difficult and inaccurate.
Despite these difficulties, advances in remote sensing offer a step forward. Satellites such as AVHRR, MODIS, VHRS, and LANDSAT monitor the distribution and disturbance in vegetation. However, predicting wildfires from these images is still a challenging task. Harnessing the potential of Artificial Intelligence (AI) and Machine Learning (ML) offers a reliable way to predict and manage wildfires. AI can learn from available data and make accurate predictions. AI linked with imaging satellites can analyze factors such as the emergence of smoke, incidence of fire, disturbance of vegetation, and correlate them with various physical parameters of the forest such as vegetation type, climate, landscape, fire susceptibility mapping, and soil deposits to predict the occurrence and pattern of wildfires. The learning of AI algorithms during the prediction process increases the accuracy of predictions. Experimental wildfire predictions made by AI algorithms have been remarkably accurate with less detection time. Software-defined cameras (SDCs) assisted with AI can predict forest fires locally by detecting smoke. AI technologies also allow for easy management of many patrol vehicles with video cameras and fire extinguishers by a single user, ensuring early detection and efficient management of forest fires. The major limitation of AI-mediated wildfire management is the lack of realistic modeling methodologies that suit the needs of multiple landscapes across the world. Additionally, sophisticated knowledge, high-quality parameters about forests, and better AI algorithms that correlate available data are also lacking. With high-resolution data and sophisticated algorithms, AI-powered machines can certainly circumvent the emergence and spread of forest fires.
This special issue aims to cover a range of topics related to the use of artificial intelligence and machine learning techniques in the management and mitigation of wildfires. Additionally, the issue will also explore ethical and regulatory considerations related to the use of AI in wildfire management, as well as potential challenges and limitations of these technologies. We invite researchers from various location across the world to submit their original papers on this background.
Suggested list of topics:
- Current trends in identifying areas at risk of wildfire and the incidence of fires in different forest regions
- Advances in using AI techniques to better understand and manage wildfires
- Machine learning strategies to enhance wildfire management in arid regions
- New developments in using neural networks for mapping and controlling wildfire in forests
- Development of sophisticated AI algorithms for early detection of smoke and prediction of wildfires
- Using AI to analyze satellite imagery of forest fires and identify patterns
- Recent trends in using AI to identify areas of forest most susceptible to wildfire
- Innovative approaches to using AI for wildfire preparedness and response
- The significance of utilizing AI for real-time monitoring of wildfires
- Exploring the potential of AI to revolutionize forestry management through data analytics.
Dr. Shabir Ahmad
Department of IT Convergence Engineering
Sujeong-Gu, Seongnam-Si 461-701, South Korea
Dr. Mouna Baklouti
National Engineering School of Sfax (ENIS)
BPW3038, Sfax, Tunisia
Dr. Sabina Umirzakova
Depart of Computer Science
Graduate School of IT
Gachon University, Seoung-nam si, South Korea
Dr. Faisal Jamil
Department of ICT and Natural Sciences
Faculty of Information Technology and Electrical Engineering
Norwegian University of Science and Technology
Larsgårdsvegen 2, 6009 Ålesund, Norway
Submit your manuscript here.