In the pursuit of a comprehensive understanding of wildfires and their escalating severity, this machine learning project embarked on an exploration of trends and models, delving into the intricacies of external factors such as weather patterns and human interventions. The core objective was to construct insights that could inform effective strategies for wildfire prevention and response. Acknowledging the substantial efforts of government agencies like FEMA and organizations like NFPA in collecting and communicating vital information on wildfires, this project sought to increase its scope by incorporating additional resources such as the US Drought Monitor data and News Headlines. By leveraging a variety of machine learning techniques and integrating data from multiple sources spanning the years 2000 to 2022, the project aimed to contribute to the ongoing efforts to understand, predict, and mitigate the impact of wildfires in the United States.

As a bridge between traditional approaches and innovative insights, the project embraced both unsupervised methods, including k-means and hierarchical clustering, to identify patterns within the data, and supervised learning models such as Naive Bayes, Support Vector Machines, Decision Trees, and Multivariate Regression analyses to predict and comprehend the factors influencing wildfires. The integration of diverse datasets from the National Interagency Fire Center (NIFC), the National Oceanic and Atmospheric Association, collaborative data sources, and the National Drought Mitigation Center demonstrate the project’s commitment to building upon existing knowledge and advancing strategies for wildfire management. With a focus on exploring the intricate relationships between weather patterns, human actions, and the evolving landscape of wildfires, the project aimed to provide nuanced insights that surpass the capabilities of conventional approaches, ultimately contributing to the broader understanding and mitigation of this formidable natural disaster. What initially seemed like a manageable goal expanded into a far-reaching and intricate endeavor, presenting challenges and complexities that surpassed the initial expectations. This conclusion discusses the successes and limitation that this project faced, and the goals for future projects in the same scope.

The comprehensive machine learning project on wildfires in the United States has provided valuable insights across various dimensions. The utilization of k-means clustering revealed distinct clusters within the datasets, indicating the effective grouping of similar data points. The choice of the optimal number of clusters, determined through techniques such as the silhouette and elbow methods, played a crucial role in ensuring meaningful and distinct clusters. This clustering analysis lays the foundation for further targeted analyses in understanding the patterns and categories related to wildfires.

The hierarchical clustering analysis, employing dendrograms, deepened the understanding of the spatial and contextual aspects of wildfires. It highlighted the severity of drought conditions, causal factors, and the narratives associated with wildfires in media and event descriptions. Particularly, the identification of scenarios involving unattended campfires, lightning strikes, and powerline-related incidents provides valuable information for wildfire events have trends of components involved. This understanding can help with creating informed efforts in reducing these particular fire causes.

Association Rule Mining offered insights into the primary causes and factors contributing to wildfires, unveiling the significant impact of natural elements like lightning and the complex interplay between various terms. The analysis of NewsAPI headlines data highlighted the lasting impact of major wildfire events on media discourse and emphasized the association of wildfires with climate change, signaling the need for ongoing awareness and attention.

The Naive Bayes supervised learning models exhibited varying levels of success and challenges across diverse datasets. The importance of adapting models to specific contexts, such as simplifying topics for news headline classification, was evident. The analysis of causes and factors influencing wildfires in Oregon showcased the multifaceted nature of predictive modeling, emphasizing the role of data preparation, feature selection, and ongoing refinement.

Decision tree models demonstrated the viability of predicting a “Fire Season” based on quantitative metrics, but their performance varied across datasets, highlighting the sensitivity to specific contexts and data characteristics. The support vector machines (SVMs) emerged as powerful tools, offering robust classification capabilities and versatility in handling different types of data. The success of SVMs in predicting the time of year for wildfires, distinguishing topics in news headlines, and identifying the general causes of wildfires in Oregon demonstrates their pattern identification in wildfire prediction and management.

Overall, this machine learning project has provided a comprehensive understanding of wildfires, encompassing clustering, hierarchical analysis, association rule mining, Naive Bayes models, decision trees, and support vector machines. The insights gained contribute to informed decision-making, proactive wildfire management strategies, and ongoing efforts to mitigate the impact of wildfires on communities and ecosystems in the United States. The project’s success and challenges highlight the dynamic nature of machine learning applications in environmental science, emphasizing the need for adaptability and continuous refinement in addressing complex and evolving datasets.