Evaluation of zenith tropospheric delays estimated by different regression models

Year 2022, Volume: 11 Issue: 3, 661 - 671, 18.07.2022

Ali Utku Akar Cevat İnal

https://doi.org/10.28948/ngumuh.1088375

Abstract

Advances in artificial intelligence and machine learning provide alternative solutions to the problems in GNSS applications or enable to increase the efficiency of existing solutions. There are many errors encountered in GNSS and these errors pose a problem to users. The tropospheric delay effect is one of them. The increasing interest in GNSS technology as well as the use of learning algorithms in atmosphere/troposphere studies have made it important to create new models for tropospheric delay estimation. In this study, it was aimed to estimate the zenith tropospheric delay (ZTD) by using Radial Basis Function-Support Vector Regression (RBF-SVR), Ridge and Elastic-Net regression models. The new regression models trained according to machine learning was investigated its preferability as an alternative in ZTD estimation. For this reason, the results obtained from different methods were analyzed by comparing the ZTD models. From the analysis results, it was determined that the RBF-SVR model gave the best results, followed by the Elastic-Net and Ridge models. Afterward, the ZTD values obtained from the new models were compared with the Canadian Spatial Reference System–Precise Point Positioning (CSRS-PPP) ZTD values and the performance of the models was evaluated. According to the evaluation results, it was concluded that RBF-SVR is the most compatible model to CSRS-PPP.

Keywords

ZTD Estimation, Machine learning, RBF-SVR, Ridge and elastic-net regression, CSRS-PPP

Farklı regresyon modelleriyle kestirilen zenit troposferik gecikmelerin değerlendirilmesi

Abstract

Yapay zekâ ve makine öğrenimi alanındaki gelişmeler, GNSS uygulamalarındaki sorunlara alternatif çözümler sunmakta veya mevcut çözümlerin verimliliğini artırmaya imkân sağlamaktadır. GNSS’de karşılaşılan birçok hata vardır ve bu hatalar kullanıcılar için problem oluşturmaktadır. Troposferik gecikme bunlardan birisidir. GNSS teknolojisine ilginin artmasıyla beraber öğrenme algoritmalarının atmosfer/troposfer çalışmalarında kullanımı, troposferik gecikme kestirimi için yeni modellerin oluşturulmasını önemli hale getirmiştir. Bu çalışmada, Radyal Tabanlı Destek Vektör Regresyonu (RTF-DVR), Ridge ve Elastik-Net regresyon modelleriyle zenit troposferik gecikmenin (ZTD) kestirilmesi amaçlanmış, makine öğrenimi esasına göre eğitilmiş yeni regresyon modellerinin ZTD kestiriminde alternatif olarak tercih edilebilirliği araştırılmıştır. Bunun için farklı yöntemlerden elde edilen sonuç ZTD modelleri karşılaştırılarak analiz edilmiştir. Analiz sonuçlarından RTF-DVR modelinin daha iyi sonuçları verdiği, bunu Elastik-Net ve Ridge modellerinin takip ettiği tespit edilmiştir. Sonrasında yeni modellerden elde edilen ZTD değerleri, Canadian Spatial Reference System–Precise Point Positioning (CSRS-PPP) ZTD değerleriyle karşılaştırılıp modellerin performansı değerlendirilmiştir. Değerlendirme sonuçlarına göre, CSRS-PPP’ye en uyumlu modelin RTF-DVR olduğu sonucuna varılmıştır.

Keywords

RTF-DVR, CSRS-PPP, ZTD kestirimi, Makine öğrenimi, RTF-DVR, Ridge ve elastik-net regresyonu, CSRS-PPP

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