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Transitioning From Legacy Systems to AI: A Holistic Approach to Disaster Detection
Abstract
Disaster detection has long been a challenge due to the complexity and dynamic nature of natural disasters. Traditional methods often lack real-time capabilities and struggle to account for these challenges. This chapter explores the transition from legacy systems to Artificial Intelligence (AI) and Machine Learning (ML) for a more holistic approach. AI and ML concepts, including supervised and unsupervised learning algorithms, can analyze vast amounts of data from various sources to identify patterns and anomalies indicative of impending disasters. The integration of plant science offers additional insights into ecosystem responses to environmental changes, further refining AI models. The chapter explores the business benefits of AI/ML in disaster detection, including cost savings, improved risk management, and enhanced resilience. It advocates for multidisciplinary collaboration among scientists, technologists, and business leaders to create a comprehensive, real-time disaster detection system.
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