A review of data analytics techniques in enhancing environmental risk assessments in the U.S. Geology Sector

Abstract

In an era where environmental risks pose significant challenges to the U.S. geology sector, this paper meticulously explores the integration of data analytics techniques to enhance risk assessments. The study delves into the intricate relationship between geological processes and human activities, underscoring the necessity for advanced analytical methodologies in mitigating environmental risks. The background sets the stage, highlighting the evolving perception of risk and sustainability in geological activities, and the critical role of reliable construction practices and engineering investigations. The aim of this paper is to synthesize and critically evaluate the current methodologies in data analytics, particularly their impact on reducing environmental risks associated with geological activities. The scope encompasses a detailed examination of the evolution from traditional to modern analytical methods, emphasizing the integration of predictive analytics, machine learning, big data, and Geographic Information Systems (GIS) in geological predictions and risk management. The main findings reveal a significant advancement in data analytics, marked by the integration of AI and machine learning with traditional geological methods. This fusion enhances the accuracy, efficiency, and comprehensiveness of risk assessments. The study concludes with recommendations for continued integration of advanced data analytics in geological studies, advocating for sustainable and responsible practices. It emphasizes the importance of international collaboration and harmonization of regulatory standards to enhance environmental risk assessments in geology. This paper provides valuable insights for researchers, policymakers, and practitioners in the field, offering a roadmap for future advancements in geological data analytics and environmental risk management.