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A Prediction of Precipitation Data Based on Support Vector Machine and Particle Swarm Optimization (PSO-SVM) Algorithms

机译：基于支持向量机和粒子群优化（PSO-SVM）算法的降水量预测。

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摘要

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.
机译：降水是天气预报中非常重要的话题。天气预报，尤其是降水预测，构成了复杂的任务，因为它们依赖于各种参数来预测因变量，例如温度，湿度，风速和风向，这些变量会不时发生变化，并且天气计算会随地理位置及其大气而变化变量。为了提高降水的预报精度，本文提出了一种基于粒子群优化支持向量机（PSO-SVM）的降水预报模型，以代替传统降水中的线性阈值。参数选择对SVM的预测精度具有至关重要的影响，提出了PSO来找到SVM的最佳参数。 PSO-SVM算法通过使用历史数据进行降水预测来训练模型，这可能是有用的信息，并且被各行各业的人们用来做出明智而明智的决策。仿真表明，预测模型表明，如果其他条件相同，则所提出算法的性能比基于一组实验数据的直接预测模型具有更好的准确性。另一方面，仿真结果证明了在机器学习中使用SVM-PSO模型的有效性和优势，并且随着越来越多的相关属性可用于预测因变量，该方法有望进一步改进。

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