## 1 Introduction
The utilization of standard AI-based data-driven models for monthly runoff prediction can enhance the prediction effect, which is of great importance in the field of predicting hydrological runoff direction. Karst areas account for 7-12% of the Earth's continental area. About a quarter of the world population's drinking water comes from karst aquifers [11]. Therefore, hydrological studies in karst areas are critical. Karst geology is widely distributed in China, and the distribution area of soluble rocks accounts for about 1/3 of the national land area. In the south of China, the distribution of karst areas is mainly in Yunnan and Guizhou provinces and Guangxi district. Additionally, the total area of karst in Guangxi is 120,000 km\({}^{2}\), accounting for half of the total area of Guangxi. Karst is formed by the dissolution of soluble rocks and its characteristics include sinkholes and caves in the underground drainage system [12]. The complex and unique topography of karst regions leads to difficulties for traditional hydrological models to fully generalize them, especially when the underlying physical relationships (or underground geographical characteristics) cannot be explicitly obtained. As can be seen in Table 1, traditional hydrological models are typically applied to specific watersheds and need large amounts of measurement information (or expert experience). Although runoff processes can be reflected in hydrologic models, measurement information and expert experience are often needed to dynamically adjust computational parametersand model structure [13]. Artificial intelligence models can provide new possibilities for runoff prediction when the underlying physical relationship cannot be explicitly obtained. However, few studies have investigated the effectiveness of artificial intelligence models for runoff prediction in karst areas. [PERSON] et al. [14] applied artificial neural networks to a typical karst region in Hubei province, China; [PERSON] et al. [15] constructed a model that combined Ensemble Empirical Mode Decomposition and the Long Short-Term Memory model to effectively improve the prediction accuracy of discharge from karst springs; [PERSON] et al. [16] used Lez basin in southern France as an example to verify the feasibility of applying neural network models to karst basins and proposed an effective method to determine the complexity of neural models; [PERSON] et al. [17] measured the stability of models based on different data structure inputs and investigated the effect of input methods on the performance of AI models in karst areas. This study helps to further bridge this gap by first investigating the performance of standard AI models and pre-processing-technique-based AI methods in karst watershed modeling, before then demonstrating superiority of triple-coupled models that apply quantile mapping to these standard coupled methods. \begin{table}
\begin{tabular}{c c c c c} \hline
**Classification** & **Name** & **Author** & **Improvements** & **Advantages and Disadvantages** \\ \hline \multirow{6}{*}{Lumped hydrological model} & \multirow{2}{*}{Xin’anjiang} & \multirow{6}{*}{[PERSON] et al. [18]} & The conversion of surface flow to underground flow in taxi areas of Central Guangzhou Province was simulated. Used three interconnected tanks to represent the spatial layer structure of the karst water-bearing medium and simulated groundwater at different velocity. Application to Fontaine de Vacuse (southern France). The sinkhole diameter function was developed based on the flow through the sinkhole in the streambed of the Kentucky karst basin. The function was added to the SWAT model to form a karst SWAT model. & & Advantages: easy acquisition of data and high calculation efficiency. Ability to simulate different hydrological processes in large complex basins using spatial information provided by GIS and RS.