FLOOD FORECASTING AND EARLY WARNING

Configuring real-time forecasting systems using MIKE OPERATIONS

This two-day, hands-on course gives you a thorough understanding of the state-of-the-art of real-time flood forecasting and early warning systems and how they help authorities disseminate timely and reliable warnings to the public. You will learn about our successful approach for creating powerful forecasting and early warning systems and how you can configure a system to meet your specific needs.

You will receive hands-on training in the MIKE OPERATIONS product. You will learn how to create the automated jobs that run behind the scene and how to configure a user interface for operators. The latter is specifically designed to cope with the peak hours of flood forecasting centres. 

The course will give you ample opportunity to share experiences and discuss relate course topics related to your own needs.

When reliable flood information can be developed quickly and safely into early warning and response actions, the benefits of forecasts and warnings increase significantly. This helps mitigate human suffering as well as reduces significantly infrastructure damages and loss of productivity.

COURSE TOPICS

  • Real-time operational flood forecasting and early warning Best Practices
  • Real-time data and information management Best Practices
  • Configuration of systems to support specific needs, including creating data interfaces to external data sources 
  • Time series analyses and visualisation 
  • Data management and auditing such as data quality and logging of data changes
  • Creating and scheduling jobs to automate common work flows  
  • Configuration of early warning system, including set-up of automated alerts and flood bulletins and reports
  • Running flood mitigation scenarios during emergency situations
  • Scripting tools based on Python for developing user defined tools

TARGET GROUP AND PREREQUISITES

Professionals such as system configurators and operators working with real-time flood forecasting and early warning systems. It is an advantage, but no condition, that participants have previous experience with water modelling, data acquisition systems, and basic ICT knowledge.