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Dergiler / Sakarya University Journal of Science / 2026 / Volume: 27 Issue: 3

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

Arzu Ozkaya(ANKARA YILDIRIM BEYAZIT ÜNİVERSİTESİ)0000-0003-3983-8831
Asım Egemen Yılmaz(ANKARA ÜNİVERSİTESİ)0000-0002-4156-4238
Sayfa
—
Yayın Tarihi
2023-06-30
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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.

📌 3 Maddede Bu Makale

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Amaç & Hipotez: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.
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Temel Bulgular & Veri:The preliminary results show that the SVR has a location-oriented performance.
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Sonuç & Katkı: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.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Support Vector Regression · weather radar data · flood

Abstract

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.

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Bu Makaleye Atıf Yap

Ozkaya, A., Yılmaz, A. E. (2023). The Support Vector Regression with L1 Norm: Application to Weather Radar Data in Adjusting Rainfall Errors. Sakarya University Journal of Science, Volume: 27 Issue: 3. https://doi.org/10.16984/saufenbilder.1090178

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