A Comparison of Three Soft Computing Techniques, Bayesian Regression, Support Vector Regression, and Wavelet Regression, for Monthly Rainfall Forecast

Sharma, Ashutosh; Goyal, Manish Kumar

doi:10.1515/jisys-2016-0065

Cited by 8 publications

(2 citation statements)

References 38 publications

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“…There are other studies that also predict rainfall using Bayesian Regression, Support Vector Regression, and Wavelet Regression. Where in this study with the SVR method the smallest RMSE value is 108.71 in rainfall forecasting [7]. There are other studies that use the Long Short-Term Memory (LSTM) and LSTM-PSO methods in predicting rainfall.…”

Section: Introductionmentioning

confidence: 68%

Comparison of Regression, Support Vector Regression (SVR), and SVR-Particle Swarm Optimization (PSO) for Rainfall Forecasting

Yulianto

Mahmudy

Soebroto

2020

JITeCS

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Rainfall is one of the factors that influence climate change in an area and is very difficult to predict, while rainfall information is very important for the community. Forecasting can be done using existing historical data with the help of mathematical computing in modeling. The Support Vector Regression (SVR) method is one method that can be used to predict non-linear rainfall data using a regression function. In calculations using the regression function, choosing the right SVR parameters is needed to produce forecasting with high accuracy. Particle Swarm Optimization (PSO) method is one method that can be used to optimize the parameters of the existing SVR method, so that it will produce SVR parameter values with high accuracy. Forecasting with rainfall data in Poncokusumo region using SVR-PSO has a performance evaluation value that refers to the value of Root Mean Square Error (RMSE). There are several Kernels that will be used in predicting rainfall using Regression, SVR, and SVR-PSO with Linear Kernels, Gaussian RBF Kernels, ANOVA RBF Kernels. The results of the performance evaluation values obtained by referring to the RMSE value for Regression is 56,098, SVR is 88,426, SVR-PSO method with Linear Kernel is 7.998, SVR-PSO method with Gaussian RBF Kernel is 27.172, and SVR-PSO method with ANOVA RBF Kernel is 2.193. Based on research that has been done, ANOVA RBF Kernel is a good Kernel on the SVR-PSO method for use in rainfall forecasting, because it has the best forecasting accuracy with the smallest RMSE value.

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Section: Introductionmentioning

confidence: 68%

Comparison of Regression, Support Vector Regression (SVR), and SVR-Particle Swarm Optimization (PSO) for Rainfall Forecasting

Yulianto

Mahmudy

Soebroto

2020

JITeCS

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“…In recent years, soft computing-based machine learning methods have been widely utilized in combination with other methods to form the ensemble and conjunction methods, which are suggested as promising rainfall prediction methods [22][23][24][25]. Several examples of such methods will be mentioned.…”

Section: Introductionmentioning

confidence: 99%

An Extreme Learning Machine and Gene Expression Programming-Based Hybrid Model for Daily Precipitation Prediction

Peng¹,

Zhao²,

Zhang³

et al. 2019

IJCIS

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Accurate daily precipitation prediction is crucially important. However, it is difficult to predict the precipitation accurately due to inherently complex meteorological factors and dynamic behavior of weather. Recently, considerable attention has been devoted in soft computing-based prediction approaches. This work presents a scheme to reduce the risk of Extreme Learning Machine (ELM) modeling error using Gene Expression Programming (GEP) to improve the prediction performance, and develops an ELM-GEP hybrid model for regional daily quantitative precipitation prediction. In this study, firstly, we use ELM for modeling the data sample of daily rainfall to construct a main model. Secondly, we use GEP for modeling the error of the main model as a compensation of the main model to reduce the prediction error. We conducted eight experiments of two different types of daily precipitation prediction problems using five metrics to evaluate our proposed model performance. Experimental results show that our model is comparable or even superior to five state-of-the-art models with high reliability in terms of all metrics on all datasets. It indicates that the proposed method is a promising alternative prediction tool for higher accuracy and credibility of regional daily precipitation prediction.

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A Soft Computing-Based Daily Rainfall Forecasting Model Using ELM and GEP

Peng

Zhao²,

et al. 2019

Proceedings in Adaptation, Learning and Optimization

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A Comparison of Three Soft Computing Techniques, Bayesian Regression, Support Vector Regression, and Wavelet Regression, for Monthly Rainfall Forecast

Cited by 8 publications

References 38 publications

Comparison of Regression, Support Vector Regression (SVR), and SVR-Particle Swarm Optimization (PSO) for Rainfall Forecasting

Comparison of Regression, Support Vector Regression (SVR), and SVR-Particle Swarm Optimization (PSO) for Rainfall Forecasting

An Extreme Learning Machine and Gene Expression Programming-Based Hybrid Model for Daily Precipitation Prediction

A Soft Computing-Based Daily Rainfall Forecasting Model Using ELM and GEP

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