Multivariate adaptive regression splines-assisted approximate Bayesian computation for calibration of complex hydrological models

Author:

Ma Jinfeng1,Li Ruonan1ORCID,Zheng Hua1,Li Weifeng1,Rao Kaifeng1,Yang Yanzheng1,Wu Bo2

Affiliation:

1. a State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 100085 Beijing, China

2. b School of Mathematics and Statistics, Ningxia University, Yinchuan 750021, China

Abstract

Abstract Approximate Bayesian computation (ABC) relaxes the need to derive explicit likelihood functions required by formal Bayesian analysis. However, the high computational cost of evaluating models limits the application of Bayesian inference in hydrological modeling. In this paper, multivariate adaptive regression splines (MARS) are used to expedite the ABC calibration process. The MARS model is trained using 6,561 runoff simulations generated by the soil and water assessment tool (SWAT) model and subsequently replaces the SWAT model to calculate the objective functions in ABC and multi-objective evolutionary algorithm (MOEA). In experiments, MARS can successfully reproduce the runoff time series simulations of the SWAT model at a low time cost, with a runoff variance determination coefficient of 0.90 as compared to the Monte Carlo method. MARS-assisted ABC can quickly and accurately estimate the parameter distributions of the SWAT model. The comparison of ABC with non-Bayesian MOEAs helps in the selection of an appropriate calibration approach.

Funder

National Natural Science Foundation of China

Publisher

IWA Publishing

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