###### Abstract  
Precipitation is a very important topic in weather forecasts. Weather forecasts, especially precipitation prediction, poses complex tasks because they depend on various parameters to predict the dependent variables like temperature, humidity, wind speed and direction, which are changing from time to time and weather calculation varies with the geographical location along with its atmospheric variables. To improve the prediction accuracy of precipitation, this context proposes a prediction model for rainfall forecast based on Support Vector Machine with Particle Swarm Optimization (PSO-SVM) to replace the linear threshold used in traditional precipitation. Parameter selection has a critical impact on the predictive accuracy of SVM, and PSO is proposed to find the optimal parameters for SVM. The PSO-SVM algorithm was used for the training of a model by using the historical data for precipitation prediction, which can be useful information and used by people of all walks of life in making wise and intelligent decisions. The simulations demonstrate that prediction models indicate that the performance of the proposed algorithm has much better accuracy than the direct prediction model based on a set of experimental data if other things are equal. On the other hand, simulation results demonstrate the effectiveness and advantages of the SVM-PSO model used in machine learning and further promises the scope for improvement as more and more relevant attributes can be used in predicting the dependent variables. support vector machine; particle swarm optimization; precipitation prediction +
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[PERSON], [PERSON], [PERSON], Y.  
area. This model derives the cluster information for the datasets by using the fuzzy C-means (FCM) clustering. The proposed method can improve the interpretability of variation in rainfall, but it cannot provide an exact solution to the problem. In addition, it is unreliable and too complicated to achieve. [PERSON] et al. [PERSON] et al. (2016) proposed an efficient decision tree algorithm to predict precipitation based on the historical climate data, obtained from the meteorological department. This model is an easily implementable tool, but the accuracy is not high, and the efficiency is low when dealing with large amounts of data. Recently, due to Support Vector Machine's (SVM's) excellent characteristics of robustness and generalization performance, many researchers considered the SVM method in order to improve the accuracy of the rainfall prediction model. [PERSON] and [PERSON] [PERSON] and [PERSON] (2017) predicted monthly rainfall in a region of China by using the SVM approach with different kernel functions. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) mentioned a novel wavelet-SVM approach to precipitation forecasting from past data. SVMs have also been recently applied to precipitation related studies, such as: [PERSON] (2018) considered a large set of novel predictive variables in the daily precipitation prediction problem and analyzed the importance of humidity and Equivalent Potential Temperature variables in the SVM approach. SVM showed excellent performance by comparison with K-nearest neighbor (KNN) and multi-layer perceptron (MLP) approaches. However, the novel predictive variables are not easy to get in daily life. [PERSON] et al. (2018) established a model of rainfall occurrence and amounts simultaneously by using support vector classifiers. In addition, they used a novel set of predictive meteorological variables that improve the classifier's performance with this problem. [PERSON] (2018) proposed a novel SVM-based technique to improve rainfall estimation using the multispectral Meteosat Second Generation (MSG) and Spinning Enhanced Visible and Infra Red Imager (SEVIRI) imagery. [PERSON] (2018) proposed a new hybrid approach integrating the physically based (Hydroologic modeling system, HEC-HMS) and data-driven (Support vector regression, SVR) models to predict the hourly runoff discharges in the Chishan Creek basin, southern Taiwan.