DEVELOPMENT OF DEEP LEARNING MODELS FOR FIRE SOURCES PREDICTION

Forest fires present a significant environmental, social, and economic challenge globally, particularly in regions with diverse ecosystems and dry climates. This study focuses on developing an advanced forest fire forecasting and extinguishing system tailored to the unique environmental conditions of Kazakhstan. The aim is to leverage machine learning algorithms to enhance prediction accuracy, optimize resource allocation, and enable real-time fire detection, thereby minimizing the devastating impacts of wildfires. Machine learning plays a central role in this research, utilizing data from historical fire records, meteorological observations, satellite imagery, and topographical information to predict fire occurrences. The study implements Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to analyze environmental factors, including temperature, humidity, wind speed. Data preprocessing techniques such as data augmentation and normalization were employed to improve the robustness and accuracy of the models. The CNN model achieved a prediction accuracy of 92%, surpassing the 89% accuracy of the RNN model, highlighting the effectiveness of deep learning in fire forecasting. Additionally, precision and recall metrics confirmed the models' ability to identify fire events with high confidence. An Area Under the ROC Curve (AUC) score of 0.95 for the CNN model and 0.93 for the RNN model demonstrated the systems' reliability in distinguishing between fire and non-fire scenarios. A key contribution of the research is the localization of predictive models, which incorporates Kazakhstan-specific data, such as land cover maps and historical fire records. This approach ensures that the models account for the country's diverse landscapes and unique climatic conditions, fostering more effective and community-oriented fire management strategies. In conclusion, the integration of advanced computational techniques with local expertise marks a critical step towards sustainable forest management in Kazakhstan. The developed system not only enhances resilience against wildfires but also serves as a blueprint for data-driven fire management strategies worldwide. Future research will focus on refining predictive models with additional environmental data and exploring new algorithms for even greater accuracy and efficiency.