SEAMLESS-WAVE - developing a revolutionary flood modeling framework

SEAMLESS-WAVE is a developing “SoftwarE infrAstructure for Multi-purpose fLood modElling at variouS scaleS” based on "WAVElets" and their versatile properties. The vision behind SEAMLESS-WAVE is to produce an intelligent and holistic modelling framework, which can drastically reduce iterations in building and testing for an optimal model setting, and in controlling the propagation of model-error due to scaling effects and of uncertainty due statistical inputs.

The current team involves researchers working at the interface between applied mathematics, hydraulic modelling and science fields, which include the following people:

• Georges Kesserwani, Research Fellow at the University of Sheffield
• James Shaw, Research Associate at the University of Sheffield
• Mohammad Kazem Sharifian, PhD student at the University of Tabriz
• Janice Lynn Ayog, PhD student at the University of Sheffield
• Mohammad Shirvani, PhD student at the University of Sheffield

Full details can be found here