Can Destruction through Pakistan’s Continuous Floods Be Prevented Using Machine Learning?

Abstract

Machine Learning involves the exploration of computer algorithms designed to enhance performance automatically through experience and data utilization. Over the past two decades, Machine Learning has been applied to decipher the intricate mathematical expressions underlying the physical processes of floods, significantly advancing prediction systems with improved efficiency and cost-effectiveness. The extensive benefits and potential of Machine Learning have garnered significant popularity among hydrologists. Researchers, aiming for more accurate and efficient prediction models, introduce innovative Machine Learning methods and hybridize existing ones. In the context of Pakistan, where flooding poses a severe natural hazard, recent events in Interior Sindh showcased the devastating impact through widespread destruction and displacement. This paper seeks to delve into the current flood detection methodologies in Pakistan and explore the potential of Machine Learning in enhancing prediction systems. Drawing from diverse sources, including journals, scientific articles, and websites, the research compiles relevant information related to floods and their prevention.