# Investigation of the EWT-PSO-SVM Model for Runoff Forecasting in the Karst Area
Building physical models based on hydrologic concepts gives process-driven models the advantage of being highly interpretable but leads to models with systematic biases and reliance on high-precision data. Therefore, the accuracy of underground hydrological characteristic data and meteorological data in the basin is an important factor affecting the process-driven model performance [5]. When the underground hydrological characteristics and historical long-term meteorological data cannot be explicitly obtained, the performance of the process-driven model is difficult to give full play [6]. Different from process-driven models, data-driven models make the models fit the data to find patterns among the data, so there is no need to build physical models. As a result, although the interpretability of data-driven models is weak, they are better able to explore the regularity among data [7]. [PERSON] et al. [4] compared the runoff forecasting results of the data-driven model Long Short Term Memory (LSTM) and the process-driven model Gridded Surface Subsurface Hydrologic Analysis (GSSHA) which demonstrated that the data-driven model exhibited better performance and robustness in forecasting and calibration. [PERSON] and [PERSON] [8] used wavelet-based artificial neural network (WANN) for daily runoff forecasting. The results of the study showed that the data-driven model WANN outperformed the process-driven model GR4J. Due to the strong generalization ability, the data-driven models have gained widespread use in predicting runoff in recent years. So far, the widely used data-driven models include artificial neural networks, extreme learning machines, genetic programming, and support vector machines [9]. Among these data-driven models, the SVM was developed by [PERSON] [10], which is based on the Vapnik-Chervonenkis dimensionality theory and the principle of structural risk minimization. The SVM model has the advantages of global optimization in theory, avoiding dimensionality disasters and small sample advantage, and thus has been widely used in runoff forecasting [11; 12]. However, due to the nonlinear and nonstationary characteristics of the runoff series, a single model is hard to capture the periodicity and regularity in the runoff series, and the predictive ability of the single model is usually limited [13]. Typically, two strategies are utilized to improve SVM monthly runoff forecasts. The first is to utilize intelligent algorithms to optimize SVM parameters, and the second is to apply data preprocessing techniques to decompose the underlying subseries within the runoff series for better capture of the runoff periodicity and regularity. The commonly used parameter optimization intelligent algorithms are genetic algorithm (GA) and particle swarm optimization (PSO). Because the PSO algorithm has fewer parameters and is easy to implement, it has found broad application in the field of runoff forecasting. [PERSON] et al. [14] applied the PSO to search for optimal parameters of the back propagation (BP) neural network and compared the forecasting results of the BP neural network with that of the PSO-BP. The comparison results indicated that the BP model optimized by the PSO algorithm provides better prediction performance. [PERSON] et al. [15] used PSO and LSTM model coupling to predict glacier runoff. According to the results, the PSO-LSTM model exhibited better forecasting accuracy than the LSTM model. [PERSON] et al. [16] developed a PSO-SVM model to forecast the flow, which improved the forecasting accuracy of a single SVM model. Therefore, this study adopted the PSO method to search for optimal parameters of the SVM model. Commonly applied techniques for data preprocessing are empirical mode decomposition (EMD) [17] and ensemble empirical mode decomposition (EEMD) [18]. EMD is a commonly used nonlinear series decomposition method and has strong adaptability, but it is easy to produce a mode mixing problem and end effect. As a solution to the mode mixing problem of EMD, EEMD was presented by [PERSON] and [PERSON] [19].