Forest Fire Detection using CNN
TL;DR
Some machine learning techniques are analyzed and the use of a CNN based Model to detect fire is proposed, resulting in a more effective approach to fire detection.
Abstract
Forest fires contribute significantly to air pollution by emitting large quantities of carbon dioxide and carbon monoxide. These fires also release fine particulate matter into the air, worsening pollution levels and diminishing air quality. The impact on vegetation and wildlife is severe, resulting in substantial ecological damage and a reduction in biodiversity. Timely detection of forest fires is crucial for minimizing their destructive effects. While IoT sensors can be employed to identify smoke and fire, they are susceptible to damage during the blaze. A more effective approach to fire detection involves machine learning techniques. Various machine learning methods, such as Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Regression, and Support Vector Machines (SVM), can be utilized for this purpose .This paper analyses some machine learning techniques and proposes the use of a CNN based Model to detect fire