Computationally-efficient Model Parameter Estimation for Rainfall-Runoff-Inundation Models Based on Bayesian Optimization

Abstract

In recent years, with the increase in heavy rainfall, the frequency of water-related disasters has been increasing. The Rainfall-Runoff-Inundation (RRI) model plays an important role for predicting flood inundations. Here, parameter optimizations are necessary to achieve accurate predictions. In the future, computationally efficient parameter optimization will be effective for enhancing the accuracy of flood forecasting by accurately estimating parameters that undergo frequent and continuous changes, while minimizing computational load. This study applied the Bayesian optimization for the RRI model to explore an efficient parameter optimization method and estimate the number of iterations required for parameter estimations. The results showed that Bayesian optimization generally reproduced the observed discharge, while reducing the necessary iterations by approximately two to three orders of magnitude compared to conventionallyused optimization methods. Moreover, by examining the posterior probabilities of the evaluation function estimated through Bayesian optimization, we demonstrated that the Bayesian optimization successfully reached to globally optimal parameters without falling into local optima.