Ecological Surveillance and Crisis Management: Early Forest Fire Prediction and Endangered Species Identification

In the realm of ecological surveillance and crisis management, a pioneering approach combining some of the advanced machine learning techniques with bioacoustic analysis is to be delved. The proposed system integrates the predictive capabilities of Mel-frequency cepstral coefficients (MFCC) and other sophisticated models of Deep Learning like CRNN which is a hybrid and composite neural network architecture that combines elements of both the convolutional layers and recurrent layers, for the dual objectives of early forest fire prediction and endangered species identification. The soundscapes of avian habitats are leveraged for early forest fire prediction, wherein a classification system discerns sounds produced by birds into two categories: Panic and normal. This real-time analysis enables timely intervention strategies in the event of potential fire outbreaks. Simultaneously, the study addresses the critical issue of endangered species identification. By employing MFCC and other sound-based models, the system recognizes and classifies species-specific vocalizations during predefined observation periods. The quantification of hits, where a detected sound precisely matches a trained species' vocal signature, serves as a decisive metric in determining the endangered status of a species.