Sharif Digital Repository

The present study aims to evaluate the flood risk of Poldokhtar city probabilistically using Monte Carlo Simulations (MCS). Two-dimensional (2D) models, which are highly accurate, have been used widely for flood modeling. However, they are not suitable for applications such as MCSs that need to be repeated many times or real-time flood forecasting applications, which require that flood inundation maps quickly be produced. In the current study, we developed a data-driven surrogate model based on the Least Squares Support Vector Machine (LS-SVM), a supervised machine learning method, to predict flood depth in order to simulate similar results to 2D hydraulic modeling. HEC-RAS was used for 2D...