Check dam layout optimization on the stream network for flood mitigation: surrogate modelling with uncertainty handling

The configuration of check dams and their numbers throughout a basin are important factors for reducing floods in downstream reaches of rivers. In this paper, a stochastic model based on surrogate modelling and Monte Carlo simulation, linked to an evolutionary optimization tool, is developed to assign the optimal sites and number of check dams on a stream network. To handle uncertainty of rainfall variables and their correlation structures, the copula method is employed and an artificial neural network (ANN) is used to emulate the computationally expensive hydrological model, HEC-HMS, within the optimization routines. The prepared modelling framework is applied to a mountainous basin to determine the arrangement of check dams in its sub-basins. The experimental results show that optimal strategies can reduce the expected value of peak flood discharges by up to 50%, with significantly lower costs or number of check dams, relative to a traditional approach with a large number of check dams in sub-basins, presenting a maximum of 21% efficiency.