A Contemporary Systematic Review of Cyberinfrastructure Systems and Applications for Flood and Drought Data Analytics and Communication

Abstract

Hydrometeorological disasters, including floods and droughts, have intensified in both frequency and severity in recent years. This trend underscores the critical role of timely monitoring, accurate forecasting, and effective warning systems in facilitating proactive responses. Today's information systems offer a vast and intricate mesh of data, encompassing satellite imagery, meteorological metrics, and predictive modeling. Easily accessible to the general public, these cyberinfrastructures simulate potential disaster scenarios, serving as invaluable aids to decision-making processes. This review collates key literature on water-related disaster information systems, underscoring the transformative impact of emerging information and Internet technologies. These advancements promise enhanced flood and drought warning timeliness and greater preparedness through improved management, analysis, visualization, and data sharing. Moreover, these systems aid in hydrometeorological predictions, foster the development of web-based educational platforms, and support decision-making frameworks, digital twins, and metaverse applications in disaster contexts. They further bolster scientific research and development, enrich climate change vulnerability frameworks, and strengthen associated cyberinfrastructures. This article delves into prospective developments in the realm of natural disasters, pinpointing primary challenges and gaps in current water-related disaster information systems, and highlighting the potential intersections with future artificial intelligence solutions.

Modeling of Harmful Algal Bloom Dynamics and Integrated Web Framework for Inland Waters in Iowa

“…The authors refer to this pre-processing step as data augmentation due to the increase in the quantity of data points. Interpolation is a data augmentation approach utilized in machine learning systems (Baydaroğlu and Demir, 2023). Essentially, the MAkima procedure relies on spline interpolation to determine the values between two given points, resulting in a finer level of discretization.…”