Deep learning techniques have a promising application in rainfall-runoff simulations

but they are limited by the availability of training samples and need coupling with a traditional hydrological model that can provide training data. Selection of coupled data and hyperparameters has a significant impact on the simulation performance of a coupled model

but it lacks deep study. In this paper

we present a rainfall-runoff simulation model by coupling different module data of the Xin’anjiang model with a bidirectional long short-term memory network and optimizing the hyperparameters using the Grey Wolf optimization algorithm

along with an application to the Dongwan watershed. The results show the model improves the simulations of daily runoffs and flood events when coupled with different data

especially runoff data and simulated flow data. The hyperparameter scheme needs to be adjusted to different coupled data

and the Grey Wolf optimization algorithm can meet such a demand. This study provides new ideas and methods for enhancing the runoff simulation capability of the coupled models.