Applying Least Squares Support Vector Regression with Genetic Algorithms for Radio-Wave Path-Loss Prediction in Suburban Environment

Abstract

AbstractThis paper presents least squares support vector regression with genetic algorithms (LS-SVRGA) models for the prediction of radio-wave path-loss in suburban environment. The least squares support vector regression (LS-SVR) model is a novel forecasting approach and has been successfully used to solve time series problems. However, the application of LS-SVR models in a radio-wave path-loss forecasting has not been widely investigated. This study aims at developing a LS-SVRGA model to forecast radio-wave path-loss data. Furthermore, in the LS-SVRGA model genetic algorithms is applied in order to select two parameters of LS-SVR models. In this study, four forecasting models, Egli, Walfisch and Bertoni (W&B), generalized regression neural networks (GRNN), and support vector regression with genetic algorithms (SVRGA) models are employed for forecasting the same data sets. Empirical results indicate that the LS-SVRGA outperforms others models in terms of forecasting accuracy. Thus, the LS-SVRGA model is an effective method for radio-wave path-loss forecasting in suburban environment.KeywordsLeast squares support vector regressiongenetic algorithmsradio-wave path-loss