Comparative analysis of the optimum cluster number determination algorithms in clustering GPS velocities

Özarpacı, Seda; Kılıç, Batuhan; Bayrak, Onur Can; Özdemir, Alpay; Yilmaz, Yalcin; Floyd, Michael

doi:10.1093/gji/ggac326

Cited by 8 publications

(3 citation statements)

References 21 publications

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“…After the first clustering application, some stations along the NAF are assigned to one of the Anatolian clusters, away from that cluster. This can be explained as the plate boundary between Eurasia and Anatolia along the NAF is more dominant as a distinguishing feature than clustering itself (Savage and Simpson 2013;Özarpacı et al 2023). We cleaned the dataset for these GNSS sites affected by the fault surface trace (1999 Izmit and Düzce earthquake regions).…”

Section: Methodsmentioning

confidence: 99%

“…In order to demonstrate the importance of accounting for the tectonic structure in the GNSS geodetic velocity field, clustering analysis, known as unsupervised machine learning, was applied to the available velocity field. Clustering analysis for this region has been previously applied (Kilic and Özarpacı, 2022;Özarpacı et al 2023). However, in these studies, the authors used a sparse GNSS velocity field previously published (Özdemir and Karslıoğlu 2019).…”

Section: Introductionmentioning

confidence: 99%

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Machine learning approach for GNSS geodetic velocity estimation

Özarpacı,

Kılıç,

Bayrak

et al. 2024

GPS Solut

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This study aimed to investigate the performance of machine learning (ML) algorithms in determining horizontal velocity at specific points using the current Global Navigation Satellite System (GNSS) velocity field. To achieve this objective, the analysis utilized the most comprehensive velocity field available for Turkey, where 70% of the GNSS velocities was allocated for training the ML algorithms, while the remaining 30% was used for testing. Contrary to the previous research, the significance of considering the tectonic structure within the study area was emphasized at this point. To determine the tectonic structure of the horizontal velocity field in the region, a preliminary clustering procedure was conducted. Subsequently, distinct ML algorithms were trained using velocity fields associated with different tectonic plates. Moreover, to investigate the impact of the tectonic domain, the entire velocity field was also tested using ML algorithms without considering the tectonic structure. Four different ML algorithms, namely, Gradient Boosting Machines (GBM), LightGBM, Random Forest (RF), and eXtreme Gradient Boosting Machines (XGBoost), were employed to estimate the horizontal velocities (east and north components). The findings imply that incorporating the tectonic structure improved the performance of machine learning predictions, as indicated by the GBM algorithm's decreased root-mean-square error values. In addition, when the tectonic structure was taken into account, the accuracy assessment values for the RF and XGBoost algorithms in the east component decreased significantly. In terms of predicting GNSS velocities, the RF algorithm exhibited the lowest root-mean-square error values compared to other algorithms. The horizontal velocity differences between averages of the reference velocity field and the RF velocity estimates are maximum 0.4 mm/yr.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning approach for GNSS geodetic velocity estimation

Özarpacı,

Kılıç,

Bayrak

et al. 2024

GPS Solut

Self Cite

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“…The silhouette coefficients have also been extracted to test the stability of Clusters. The silhouette analysis is employed to select an optimal standard for n-clusters [73], [74]. It also illustrates the stability of clusters.…”

Section: Clustering Analysismentioning

confidence: 99%

Sampling Fingerprints From Multimedia Content Resource Clusters

Rashid,

Naseer,

Khan

et al. 2023

IEEE Access

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Nowadays, the growth of multimedia content over the web is exponential. The fingerprints are inconspicuously embedded in multimedia content. The fingerprints can be exploited to trace divergent information from multimedia resources. Sampling fingerprints, particularly from multimedia resources, is challenging since they are complex, heterogeneous, and diverse. This research proposed an approach to sample fingerprints from multimedia resources. Our approach partitions the multimedia content space into converged clusters using variations of Canberra distance and identifies the most diverged samples using Kullback-Leibler (KL) divergence. The resultant clusters represent the information belonging to particular concepts and the diverged samples within the clusters represent multimedia fingerprints. The fingerprint sampling process is leveraged using unsupervised learning algorithms, instantiated across various multimedia descriptors, and tested over standard multimedia datasets. The average results obtained over various standard visual and acoustic datasets reveal 80%, 77%, and 78% accuracy, precision, and recall, respectively, surpassing most of the existing baseline clustering methods such as K-Means, Mean-Shift, and DBSCAN. Furthermore, the rigorousness of the proposed algorithm clustering is evaluated using the internal clustering stability silhouette coefficient and the fingerprint diversity scores. The results unveil a maximum of 94% diversity score. The proposed variation of Canberra distance and KL divergence provides the most stable performance (SD=0.02) and creates promising implications in future multimedia retrieval, summarization, and exploration activities.

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