A Comparison of Three Soft Computing Techniques, Bayesian Regression, Support Vector Regression, and Wavelet Regression, for Monthly Rainfall Forecast
Rainfall, being one of the most important components of the hydrological cycle, plays an extremely important role in agriculture-based economies like India. This paper presents a comparison between three soft computing techniques, namely Bayesian regression (BR), support vector regression (SVR), and...
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doaj-2c2380df16f742bda0d504f84d9ba54f2021-09-06T19:40:37ZengDe GruyterJournal of Intelligent Systems0334-18602191-026X2017-09-0126464165510.1515/jisys-2016-0065A Comparison of Three Soft Computing Techniques, Bayesian Regression, Support Vector Regression, and Wavelet Regression, for Monthly Rainfall ForecastSharma Ashutosh0Goyal Manish Kumar1Department of Civil Engineering, Indian Institute of Technology, Guwahati 781039, IndiaDepartment of Civil Engineering, Indian Institute of Technology, Guwahati 781039, India, Tel.: +91 361 258 3328Rainfall, being one of the most important components of the hydrological cycle, plays an extremely important role in agriculture-based economies like India. This paper presents a comparison between three soft computing techniques, namely Bayesian regression (BR), support vector regression (SVR), and wavelet regression (WR), for monthly rainfall forecast in Assam, India. A WR model is a combination of discrete wavelet transform and linear regression. Monthly rainfall data for 102 years from 1901 to 2002 at 21 stations were used for this study. The performances of different models were evaluated based on the mean absolute error, root mean square error, correlation coefficient, and Nash-Sutcliffe efficiency coefficient. Based on model statistics, WR was found to be the most accurate followed by SVR and BR. The efficiencies for the BR, SVR, and WR models were found to be 32.8%, 52.9%, and 64.03%, respectively. From the spatial analysis of model performances, it was found that the models performed best for the upper Assam region followed by lower, southern, and middle regions, respectively.https://doi.org/10.1515/jisys-2016-0065rainfall predictionsupport vector regressionwavelet regressionbayesian regression |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sharma Ashutosh Goyal Manish Kumar |
spellingShingle |
Sharma Ashutosh Goyal Manish Kumar A Comparison of Three Soft Computing Techniques, Bayesian Regression, Support Vector Regression, and Wavelet Regression, for Monthly Rainfall Forecast Journal of Intelligent Systems rainfall prediction support vector regression wavelet regression bayesian regression |
author_facet |
Sharma Ashutosh Goyal Manish Kumar |
author_sort |
Sharma Ashutosh |
title |
A Comparison of Three Soft Computing Techniques, Bayesian Regression, Support Vector Regression, and Wavelet Regression, for Monthly Rainfall Forecast |
title_short |
A Comparison of Three Soft Computing Techniques, Bayesian Regression, Support Vector Regression, and Wavelet Regression, for Monthly Rainfall Forecast |
title_full |
A Comparison of Three Soft Computing Techniques, Bayesian Regression, Support Vector Regression, and Wavelet Regression, for Monthly Rainfall Forecast |
title_fullStr |
A Comparison of Three Soft Computing Techniques, Bayesian Regression, Support Vector Regression, and Wavelet Regression, for Monthly Rainfall Forecast |
title_full_unstemmed |
A Comparison of Three Soft Computing Techniques, Bayesian Regression, Support Vector Regression, and Wavelet Regression, for Monthly Rainfall Forecast |
title_sort |
comparison of three soft computing techniques, bayesian regression, support vector regression, and wavelet regression, for monthly rainfall forecast |
publisher |
De Gruyter |
series |
Journal of Intelligent Systems |
issn |
0334-1860 2191-026X |
publishDate |
2017-09-01 |
description |
Rainfall, being one of the most important components of the hydrological cycle, plays an extremely important role in agriculture-based economies like India. This paper presents a comparison between three soft computing techniques, namely Bayesian regression (BR), support vector regression (SVR), and wavelet regression (WR), for monthly rainfall forecast in Assam, India. A WR model is a combination of discrete wavelet transform and linear regression. Monthly rainfall data for 102 years from 1901 to 2002 at 21 stations were used for this study. The performances of different models were evaluated based on the mean absolute error, root mean square error, correlation coefficient, and Nash-Sutcliffe efficiency coefficient. Based on model statistics, WR was found to be the most accurate followed by SVR and BR. The efficiencies for the BR, SVR, and WR models were found to be 32.8%, 52.9%, and 64.03%, respectively. From the spatial analysis of model performances, it was found that the models performed best for the upper Assam region followed by lower, southern, and middle regions, respectively. |
topic |
rainfall prediction support vector regression wavelet regression bayesian regression |
url |
https://doi.org/10.1515/jisys-2016-0065 |
work_keys_str_mv |
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