The purpose of this concept and working in-progress paper is to study the catastrophic impact of wildfire on the vulnerable population of California who had to evacuate. This study intends to investigate the catastrophic effects of wildfire on the mental and physical health of the people who had to evacuate. The research will conduct an evaluation on those who had to evacuate and those who did not evacuate. As part of the study, past research work has been studied for the proposed study that can be used as a steppingstone to conduct and expand research with additional research questions. Through the literature survey and previous studies several hypotheses have been formulated. To perform the required online survey permission will be obtained from Institutional Review Board’s (IRB) conduct the proposed study.

Constant advancement in computing technology has led to an exponential increase in the volume of data stored over the past two decades. As Artificial Intelligence (AI), big data, and machine learning become a prominent part of society, these technologies are highly dependent on data as a gratuity. The development of AI technology depends on large volumes of data for learning algorithms to process and draw multiple entities, relationships, and clusters. This demand for enormous data has increased the need for efficient data storage and retrieval methods. The archival process is a long-standing tradition starting from libraries. Data archival is a process of gathering, storing, and managing historical records. Artificial Intelligence advancements have developed insight into integrating AI into the data archival process. This research explores the use of AI in data archival processes. Across multiple fields, data archival is required, such as in academic journals, institutional archival, and many more. AI can be used in activities such as progression, description, search, and record-keeping. This research explores the multifaceted integration of AI into each step of the archival process, aiming to advance the field by enhancing data accessibility, efficiency, and overall effectiveness by reviewing the current literature and models to explain the trends related to AI services used for data archival.