A randomised approach to flood control using Value-at-Risk

Flooding is one of the major risks associated with rivers, and a typical operational goal is to reduce the risk of severe floods while at the same time not being overly cautious. In this paper we consider a Stochastic Model Predictive Control (S-MPC) based strategy which is well suited for rivers with uncertain in- and out-flows. In order to reduce the risk of floods, Value-at-Risk (VaR) is used as a risk measure and incorporated as a chance-constraint in the control optimisation problem. A computationally tractable scenario-based iterative optimisation and testing algorithm is proposed for solving the corresponding S-MPC problem, and its usefulness is demonstrated on a simulated river example.