Harnessing the Power of Machine Learning for Forest Fire Management: A Review

By Dr. Sampath LonkaReviewed by Susha Cheriyedath, M.Sc.Jul 26 2023

In a paper published in the journal Applied Sciences, researchers conducted a comprehensive review of various machine learning (ML) methods for fire prediction while also acknowledging the challenges in model selection. By identifying research gaps and recent studies aids, they aimed to enhance prediction accuracy by tailoring ML techniques to specific forest characteristics.

Background

Forest fires are vital components of the Earth's system, occurring worldwide throughout the year. Around 420 Mha, an area larger than India, is burned annually, with grasslands and savannas most affected. Human activities cause over 90% of these fires, while lightning accounts for the rest.

Forest fires have far-reaching effects, impacting global warming and the survival of plant and animal life. Early prediction and detection are crucial to limiting damage and firefighting efforts. Remote sensing technology and ML have significantly improved forest fire observation and monitoring. Applying ML methods in forest fire management and science has gained attention in recent years, promising further advancements in research.

ML process

The rapid advancements in artificial intelligence (AI) and ML have made processes more precise, manageable, and cost-effective. ML, a subset of AI, employs data-trained algorithms to predict outcomes and uncover hidden patterns. Three primary categories of ML methods exist supervised, unsupervised, and reinforcement learning. The ML process includes data gathering, feature selection, model building, and system evaluation. Deep learning (DL), a branch of ML, utilizes artificial neural networks to enable computers to learn patterns and make decisions like humans, facilitating deep insights from data. Convolutional neural networks (CNNs) are particularly effective in fire detection systems, including forest fire prediction using CNNs.

Review of applications in forest fire science using ML

ML technologies have gained popularity in forest fire science, with numerous studies exploring their applications in various domains. One crucial area of interest is fire detection, where researchers have leveraged ML methods for early detection.

For instance, a study utilized multi-source data, including geographic, meteorological, visual, and infrared camera data, achieving a 98% detection rate and a 1.93% false alarm rate using various ML algorithms. Another study employed distributed artificial neural networks (ANN) and Naive Bayes to analyze forest fire and residential fire data, attaining 92% and 81% detection accuracies, respectively.

Moreover, ML approaches have been applied to predicting forest fires. Logistic regression was used to forecast fire ignition based on terrain, vegetation, meteorological conditions, and human activities, achieving an 85.7% accuracy rate. Long-short term memory classifiers were used to evaluate weather and forest fire data, predicting fire scale with 90.9% accuracy.

Fire mapping has also benefited from ML techniques. A study used UAV imagery and the Random Forest classifier to map fire severity with 89% accuracy. Additionally, support vector mechanisms (SVM) were employed to map burned areas, achieving an accuracy of over 93%.

Data collected from forest fires has been extensively evaluated using various ML methods. CNN was used to evaluate the IRIS dataset, but the outcomes were not reported. The Bi-CNN method achieved a mean accuracy of 93% in evaluating the Yupenn, BUAA, and Maryland datasets.

Overall, integrating ML technologies in forest fire science has shown promising results across detection, mapping, prediction, and data evaluation. These advancements hold significant potential for enhancing forest fire management and prevention strategies, leading to more effective and efficient approaches to combating this natural disaster. As ML technologies continue to evolve, further breakthroughs in forest fire research can be expected, ultimately contributing to a safer and more sustainable environment.

Challenges and potential for integrating ML in forest fire science

The current study emphasizes the fusion of ML with forest fire science, highlighting its potential and addressing challenges. ML offers valuable fire science and management tools, encompassing data considerations, model selection, and accuracy across various fire domains.

Collaborative frameworks involving governments and fire-prone communities must prioritize extensive data measurement and analysis to enhance forest fire resilience. Research should delve into community social media interactions and crowdsourced event sensing.

Managing vast remote sensing datasets necessitates cloud computing platforms like Google Earth Engine, which streamline data processing through RS algorithms for image enhancement, classification, and cloud masking. ML's data-centric modeling requires ample, high-quality data, prompting the utilization of synthetic datasets to bolster model performance in data-scarce scenarios.

Remote sensing and UAVs play pivotal roles in forest fire domains, enabling accurate fire location detection and real-time monitoring. Combining UAVs with ML efficiently detects forest fires and communicates crucial information. DL algorithms gain popularity for superior performance in spatial feature recognition, benefiting forest fire applications by capturing complex data structures and enhancing pattern recognition.

Conclusion                                                      

In conclusion, integrating ML into forest fire science holds promise for predicting and preventing significant environmental issues through advanced AI systems. However, addressing challenges like large datasets, high computing power requirements, and real-world accuracy evaluation necessitates expertise in forest fire research for realistic modeling. The study provides an overview of the current state and ongoing research on forest fire threats.