###### Abstract
Take SVM for accurate rainfall-runoff modeling. After a lot of work using the SVM algorithm has been done on the precipitation forecast, there is still some open research for improving the performance of the systems. Above all, in this paper, we propose a prediction model for rainfall forecasts based on Support Vector Machine (SVM). The algorithm shows many unique advantages in solving small samples, nonlinear and high dimensional pattern recognitions. The learning problem of SVM can be expressed as a convex optimization problem, so we can find the global minimum of the objective function by using the known effective algorithm [PERSON] and [PERSON] (2018). The SVM is very efficient since there only two parameters that need to be optimized. It also avoids the dimensionality of the disaster owing to the fact that the final model is only related to the support vector. SVM is a data mining technique based on machine learning used for data classification. One may use a classification algorithm to predict whether the particular day will be sunny or rainy. SVM is one of the most advanced classification techniques [PERSON] and [PERSON] (2017). Benchmarking studies have shown that the SVM performs the best among current classification techniques [PERSON] et al. (2016). Numerous experiments have shown that Support Vector Machine has satisfactory classification accuracies under a limited number of training samples, and it has been widely used in classification and prediction [PERSON] (2018); [PERSON] (2018); [PERSON] (2018); [PERSON] (2018); [PERSON] (2017); [PERSON] (2017). However, there is still a problem that the proper selection of kernel function and its parameters has great influence on the final prediction accuracy. In order to choose the optimal parameters when building the model, some researchers have linked Particle Swarm Optimization algorithms to classical SVM algorithms [PERSON] and [PERSON] (2017); [PERSON] and [PERSON] (2017); [PERSON] and [PERSON] (2017); [PERSON] and [PERSON] (2017); [PERSON] and [PERSON] (2017). The Particle Swarm Optimization (PSO) algorithm was developed by [PERSON] and [PERSON] and is a population-based stochastic optimization technique inspired by social behavior of bird flocking or fish schooling [PERSON] and [PERSON] (1983). The PSO initializes the system with a population of random solutions and searches for optima by updating generations. The particles are the potential solutions, by optimizing the particles to find the best particle is the optimal solution to the problem. Compared with other stochastic optimization techniques like Genetic Algorithms (GAs), the advantages of PSO are that it is easy to implement and there are few parameters to adjust [PERSON] and [PERSON] (2017). This technique can find the optimum parameters for the SVM model to better perform precipitation prediction. The PSO technique has been used widely in the optimization of SVM. For instance, [PERSON] and [PERSON] [PERSON] and [PERSON] (2017) proposed a PSO-SVM model to improve classification accuracies with an appropriate feature subset. [PERSON] and [PERSON] [20] take the global optimal search through PSO to obtain the SVM to effectively identify the geomechanical parameters. [PERSON] et al. [26] proposed a practical new hybrid model for short term electrical load forecasting based on PSO-SVM. The PSO algorithm looks for optimal parameters in the prediction region for the SVM model. In this way, it can be more effective to overcome the problem of local optimum. Thus, the PSO-SVM model is used for precipitation prediction. A hybrid PSO optimized SVM (PSO-SVM) model was used as an automated learning tool, trained in order to predict other parameters such as optical density, dissolved oxygen concentration, pH, nitrate concentration and phosphate concentration [27]. The solving of SVM involves quadratic programming, and the computational complexity rapidly increases as the training samples increase in number. In this paper, SVM-PSO has outstanding classification precision.