Data mining platform for fire research and machine learning

Principal Investigators: Ben Reed, Adam Kochanski

IAB Mentor: Invited: Scott Strenful (PGE) and Brian D’Agnosto (SDGE)

In this project, our aim is to revolutionize the storage, organization, and analysis of heterogeneous fire datasets. We propose to develop an intelligent fire data repository that will fundamentally transform the current approach. The primary objective of this undertaking is to create a system that efficiently manages vast volumes of data routinely collected during fire forecasting operations, long-term climate analyses, and fire experiments. By implementing this innovative solution, we will enable effective data mining, knowledge generation, and machine learning techniques. Our ultimate goal is to establish a flexible and scalable framework capable of leveraging big data to extract critical information pertinent to fire risk assessment, fire vulnerability analysis, and fire model evaluation. Through the development of this advanced repository, we seek to enhance our understanding of fires and utilize data-driven insights to improve fire management strategies.