The Support Vector Regression with L1 Norm: Application to Weather Radar Data in Adjusting Rainfall Errors

Arzu Ozkaya; Asım Egemen Yılmaz

Makale detayları

The Support Vector Regression with L1 Norm: Application to Weather Radar Data in Adjusting Rainfall Errors

Yazarlar:

Arzu Ozkaya | Asım Egemen Yılmaz

Yayıncı:

Sakarya University

Ana Başlıklar:

Civil Engineering

Dergi:

Sakarya University Journal of Science Vol.27 Issue 3, pp.621-633

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001292193

pISSN:

2147-835X

Anahtar kelimeler:

Özet:

 In hydrological research, accurate rainfall data is the primary subject for the minimization of potential loss of life and property that is mainly caused by floods. However, there is a difficulty in getting precise rainfall data for poorly gauged locations, especially in mountainous areas. Weather radar instruments can be the remedy accompanied by some errors. And, these errors should be removed before the implementation of this product. This paper presents the results of the research on radar rainfall estimate errors with support vector regression SVR method using the observed rain gauge data. The paper depicts the methodological base of the algorithm that covers additive and multiplicative corrections and the results of practical implementations considering the locations of gauge measurements. The preliminary results show that the SVR has a location-oriented performance. The multiplicative and additive correction factors show decreasing and polynomial trends respectively, as the distance from the radar location increase. Another particular outcome is that the SVR shows better results for the stations located in the mid-range mainly for 40-60 km contrary to the nearest ones. Since the systematic error in the radar data is nonlinear, the SVR method would show a promising result with a combination of other optimization techniques.

 In hydrological research, accurate rainfall data is the primary subject for the minimization of potential loss of life and property that is mainly caused by floods. However, there is a difficulty in getting precise rainfall data for poorly gauged locations, especially in mountainous areas. Weather radar instruments can be the remedy accompanied by some errors. And, these errors should be removed before the implementation of this product. This paper presents the results of the research on radar rainfall estimate errors with support vector regression SVR method using the observed rain gauge data. The paper depicts the methodological base of the algorithm that covers additive and multiplicative corrections and the results of practical implementations considering the locations of gauge measurements. The preliminary results show that the SVR has a location-oriented performance. The multiplicative and additive correction factors show decreasing and polynomial trends respectively, as the distance from the radar location increase. Another particular outcome is that the SVR shows better results for the stations located in the mid-range mainly for 40-60 km contrary to the nearest ones. Since the systematic error in the radar data is nonlinear, the SVR method would show a promising result with a combination of other optimization techniques.

 In hydrological research, accurate rainfall data is the primary subject for the minimization of potential loss of life and property that is mainly caused by floods. However, there is a difficulty in getting precise rainfall data for poorly gauged locations, especially in mountainous areas. Weather radar instruments can be the remedy accompanied by some errors. And, these errors should be removed before the implementation of this product. This paper presents the results of the research on radar rainfall estimate errors with support vector regression SVR method using the observed rain gauge data. The paper depicts the methodological base of the algorithm that covers additive and multiplicative corrections and the results of practical implementations considering the locations of gauge measurements. The preliminary results show that the SVR has a location-oriented performance. The multiplicative and additive correction factors show decreasing and polynomial trends respectively, as the distance from the radar location increase. Another particular outcome is that the SVR shows better results for the stations located in the mid-range mainly for 40-60 km contrary to the nearest ones. Since the systematic error in the radar data is nonlinear, the SVR method would show a promising result with a combination of other optimization techniques.

 In hydrological research, accurate rainfall data is the primary subject for the minimization of potential loss of life and property that is mainly caused by floods. However, there is a difficulty in getting precise rainfall data for poorly gauged locations, especially in mountainous areas. Weather radar instruments can be the remedy accompanied by some errors. And, these errors should be removed before the implementation of this product. This paper presents the results of the research on radar rainfall estimate errors with support vector regression SVR method using the observed rain gauge data. The paper depicts the methodological base of the algorithm that covers additive and multiplicative corrections and the results of practical implementations considering the locations of gauge measurements. The preliminary results show that the SVR has a location-oriented performance. The multiplicative and additive correction factors show decreasing and polynomial trends respectively, as the distance from the radar location increase. Another particular outcome is that the SVR shows better results for the stations located in the mid-range mainly for 40-60 km contrary to the nearest ones. Since the systematic error in the radar data is nonlinear, the SVR method would show a promising result with a combination of other optimization techniques.

Makale detayları

The Support Vector Regression with L1 Norm: Application to Weather Radar Data in Adjusting Rainfall Errors

Yazarlar:

Arzu Ozkaya | Asım Egemen Yılmaz

Yayıncı:

Sakarya University

Ana Başlıklar:

Civil Engineering

Dergi:

Sakarya University Journal of Science Vol.27 Issue 3, pp.621-633

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001292193

pISSN:

2147-835X

Support Vector Regression

weather radar data

flood

error minimization

Sayı

Makale detayları

The Support Vector Regression with L1 Norm: Application to Weather Radar Data in Adjusting Rainfall Errors

Yazarlar: Arzu Ozkaya | Asım Egemen Yılmaz

Yayıncı: Sakarya University

Ana Başlıklar: Civil Engineering

Dergi: Sakarya University Journal of Science Vol.27 Issue 3, pp.621-633

Belge Tipi: Makale

Yayın Tarihi: 6/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001292193

pISSN: 2147-835X

Support Vector Regression

weather radar data

flood

error minimization

Sayı

Yazarlar:

Arzu Ozkaya | Asım Egemen Yılmaz

Yayıncı:

Sakarya University

Ana Başlıklar:

Civil Engineering

Dergi:

Sakarya University Journal of Science Vol.27 Issue 3, pp.621-633

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001292193

pISSN:

2147-835X