## 1 Introduction  
Water resources are vital to human survival and development, and are essential to sustainable development. Runoff changes affect issues such as reservoir operation and water resource allocation, which in turn affect society's economic development [1]. With climate change and human activities becoming increasingly intense, runoff change has become an issue of great concern to hydrologists. Scholars in the field of hydrology have conducted a great deal of research that has promoted the rapid development of hydrological models. To summarize these previous scientific achievements, the existing popular hydrological models mainly include \"process-driven models\" and \"data-driven models\". The \"process-driven models\" consist of experimental formulations based on physics [2], which are usually less scalable and less generalizable due to time and space constraints. By contrast, the main factor considered in \"data-driven models\" is not the natural hydrological cycle but the relationships that exist within hydrological data, which are then used to build optimal models of the data through iteration and adjustment of model parameters. \"Standard artificial intelligence models\" play a large part in the application of \"data-driven models\" and have great potential [3]. Standard artificial intelligence models have the advantages of ease of compatibility, operation, and implementation, so they are widely used in runoff prediction. [PERSON] et al. [4] used a long short-term memory neural network for runoff prediction in western U.S. rivers, showing the future promise of neural networks in hydrologic prediction. [PERSON] et al. [5] explored the possibility of using the support vector machine (SVM) model in runoff prediction. From the results, it can be seen that the SVM model is not only suitable for small-sample situations, but also superior in terms of performance to other runoff-prediction techniques in the Amameh watershed. Based on normalized information measurement and dataset base transformation, [PERSON] et al. [6] proposed an adaptive runoff-prediction method using an Elman neural network (ENN), and evaluated the method using the runoff process of the Chengdu Hydropower Station from 2007 to 2011. The results showed that the maximum relative error of multiple predictions was less than 16% and that the prediction was excellent. However, there is still much room for improvement, because traditional AI models have issues with parameter optimization and prediction accuracy; attempts to address these worries led to the creation of the coupled model. A large number of scholars have researched the combined predictions of standard AI models coupled with pre-processing techniques. [PERSON] et al. [7] eliminated redundant noise from the original runoff sequence using the empirical wavelet transform (EWT), then identified the inputs of artificial neural network (ANN) models using a partial autocorrelation function, and finally used these to construct a hybrid EWT-ANN model. The hybrid model has been applied to runoff observations at four hydrological stations on the Yangtze River, China. [PERSON] et al. [8] investigated the accuracy of a hybrid model combining the long short-term memory model with the ant lion optimization model in monthly runoff predictions. The results demonstrated that this hybrid model had higher accuracy than the long short-term memory neural network model alone. In order to improve the accuracy of runoff prediction, some scholars apply post-processing methods to correct for deviation. [PERSON] et al. [9] applied the Bayesian Model Averaging post-processing method to runoff predictions in the Ful River basin, and showed that this method could improve the performance of raw multi-model grand ensemble runoff predictions. [PERSON] et al. [10] conducted a study to improve the quality of low-flow predictions in several catchments in Switzerland. The results showed that the Quantile Regression Neural Networks method played a significant role in improving the prediction quality.