A prediction of precipitation data based on support vector machine and particle swarm optimization (PSO-SVM) algorithms
type: Item
instance of: scholarly article
title: A prediction of precipitation data based on support vector machine and particle swarm optimization (PSO-SVM) algorithms
zbMATH Open document ID: 1461.62201
author: Jinglin Du
author: Yayun Liu
author: Yanan Yu
author: Weilan Yan
published in: Algorithms
publication date: 2018-08-20T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 62P12
Mathematics Subject Classification ID: 62M20
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 6920643
zbMATH Keywords: support vector machine
zbMATH Keywords: particle swarm optimization
zbMATH Keywords: precipitation prediction
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.3390/a10020057
OpenAlex ID: W2615493097
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DOI: 10.3390/A10020057
description: scientific article; zbMATH DE number 6920643