Flood Markup Language – A standards-based exchange language for flood risk communication. (June 2022) - Xiang, Zhongrun; Demir, Ibrahim
Abstract: Floods are one of the most frequently occurring natural disasters. There are numerous studies devoted to comprehending and forecasting flooding in order to aid in preparedness and response. It is critical to share and communicate datasets generated by various systems and organizations for flood forecasting and modeling. The majority of organizations share limited metadata and details for flood risk data to support research and operational activities. However, there is no standardized way for various stakeholders and automated systems to exchange flood forecast and alert data. This article proposes the Flood Markup Language (FloodML) as a data communication specification for extensively describing and exchanging flood forecasts and alerts with corresponding stakeholders. FloodML is applicable to a broad range of data sharing use cases and requirements for emergency management, the research and modeling communities, and the general public. Highlights: We developed a markup language named FloodML and data exchange framework for flood information, forecast, and alerts data. FloodML provides high practicality in integration, visualization, and exchange for effective flood data communication. Automation of data processing using FloodML can speed up the distribution of crucial information to the public. With the open-source release, we plan collect community feedback to improve FloodML specification and use cases in future.