Carsten Riggelsen

1.2k total citations
21 papers, 827 citations indexed

About

Carsten Riggelsen is a scholar working on Artificial Intelligence, Geophysics and Civil and Structural Engineering. According to data from OpenAlex, Carsten Riggelsen has authored 21 papers receiving a total of 827 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 8 papers in Geophysics and 6 papers in Civil and Structural Engineering. Recurrent topics in Carsten Riggelsen's work include Bayesian Modeling and Causal Inference (5 papers), Seismology and Earthquake Studies (5 papers) and Seismic Performance and Analysis (5 papers). Carsten Riggelsen is often cited by papers focused on Bayesian Modeling and Causal Inference (5 papers), Seismology and Earthquake Studies (5 papers) and Seismic Performance and Analysis (5 papers). Carsten Riggelsen collaborates with scholars based in Germany, Netherlands and United Kingdom. Carsten Riggelsen's co-authors include Frank Scherbaum, Élise Delavaud, Nicolas Kuehn, Kristin Vogel, Bruno Merz, Heidi Kreibich, Kai Schröter, Matthias Ohrnberger, Oliver Korup and Ad Feelders and has published in prestigious journals such as Water Resources Research, Geophysical Journal International and Bulletin of the Seismological Society of America.

In The Last Decade

Carsten Riggelsen

21 papers receiving 778 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Carsten Riggelsen Germany 14 475 352 212 116 89 21 827
Anooshiravan Ansari Iran 15 424 0.9× 466 1.3× 39 0.2× 85 0.7× 34 0.4× 41 677
Graeme Weatherill Germany 21 1.2k 2.4× 1.1k 3.1× 74 0.3× 260 2.2× 58 0.7× 54 1.6k
Torsten Riedlinger Germany 9 47 0.1× 117 0.3× 195 0.9× 99 0.9× 137 1.5× 37 613
D. Monelli Switzerland 9 740 1.6× 656 1.9× 48 0.2× 156 1.3× 60 0.7× 11 1.1k
Nirmal Jayaram United States 8 1.5k 3.2× 329 0.9× 36 0.2× 36 0.3× 27 0.3× 9 1.6k
Mehdi Zaré Iran 18 748 1.6× 979 2.8× 60 0.3× 206 1.8× 63 0.7× 99 1.4k
Tomoyuki Takabatake Japan 20 427 0.9× 263 0.7× 158 0.7× 34 0.3× 261 2.9× 77 1.0k
F. Mele Italy 15 286 0.6× 809 2.3× 36 0.2× 237 2.0× 19 0.2× 38 1.0k
Ufuk Hancılar Türkiye 12 397 0.8× 177 0.5× 78 0.4× 68 0.6× 64 0.7× 26 581
Scott J. Brandenberg United States 22 1.6k 3.4× 275 0.8× 45 0.2× 53 0.5× 48 0.5× 135 1.8k

Countries citing papers authored by Carsten Riggelsen

Since Specialization
Citations

This map shows the geographic impact of Carsten Riggelsen's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Carsten Riggelsen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Carsten Riggelsen more than expected).

Fields of papers citing papers by Carsten Riggelsen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Carsten Riggelsen. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Carsten Riggelsen. The network helps show where Carsten Riggelsen may publish in the future.

Co-authorship network of co-authors of Carsten Riggelsen

This figure shows the co-authorship network connecting the top 25 collaborators of Carsten Riggelsen. A scholar is included among the top collaborators of Carsten Riggelsen based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Carsten Riggelsen. Carsten Riggelsen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Vogel, Kristin, Carsten Riggelsen, Oliver Korup, & Frank Scherbaum. (2014). Bayesian network learning for natural hazard analyses. Natural hazards and earth system sciences. 14(9). 2605–2626. 39 indexed citations
2.
Schröter, Kai, Heidi Kreibich, Kristin Vogel, et al.. (2014). How useful are complex flood damage models?. Water Resources Research. 50(4). 3378–3395. 144 indexed citations
3.
Kuehn, Nicolas, et al.. (2013). Simultaneous quantification of epistemic and aleatory uncertainty in GMPEs using Gaussian process regression. Bulletin of Earthquake Engineering. 12(1). 449–466. 23 indexed citations
4.
Scherbaum, Frank, et al.. (2013). An Interactive Tool for the Elicitation of Subjective Probabilities in Probabilistic Seismic-Hazard Analysis. Bulletin of the Seismological Society of America. 103(5). 2862–2874. 14 indexed citations
5.
Douglas, John, Sinan Akkar, G. Ameri, et al.. (2013). Comparisons among the five ground-motion models developed using RESORCE for the prediction of response spectral accelerations due to earthquakes in Europe and the Middle East. Bulletin of Earthquake Engineering. 12(1). 341–358. 65 indexed citations
6.
Kuehn, Nicolas, et al.. (2012). Learning Task Relatedness via Dirichlet Process Priors for Linear Regression Models. The European Symposium on Artificial Neural Networks. 1 indexed citations
7.
Vogel, Kristin, Carsten Riggelsen, Bruno Merz, Heidi Kreibich, & Frank Scherbaum. (2012). Flood Damage and Influencing Factors: A Bayesian Network Perspective. Publication Database GFZ (GFZ German Research Centre for Geosciences). 19 indexed citations
8.
Riggelsen, Carsten & Matthias Ohrnberger. (2012). A Machine Learning Approach for Improving the Detection Capabilities at 3C Seismic Stations. Pure and Applied Geophysics. 171(3-5). 395–411. 17 indexed citations
9.
Ohrnberger, Matthias, et al.. (2011). Bayesian networks for tsunami early warning. Geophysical Journal International. 185(3). 1431–1443. 29 indexed citations
10.
Kuehn, Nicolas, Carsten Riggelsen, & Frank Scherbaum. (2011). Modeling the Joint Probability of Earthquake, Site, and Ground-Motion Parameters Using Bayesian Networks. Bulletin of the Seismological Society of America. 101(1). 235–249. 21 indexed citations
11.
Scherbaum, Frank, Élise Delavaud, & Carsten Riggelsen. (2009). Model Selection in Seismic Hazard Analysis: An Information-Theoretic Perspective. Bulletin of the Seismological Society of America. 99(6). 3234–3247. 280 indexed citations
12.
Kuehn, Nicolas, Frank Scherbaum, & Carsten Riggelsen. (2009). Deriving Empirical Ground-Motion Models: Balancing Data Constraints and Physical Assumptions to Optimize Prediction Capability. Bulletin of the Seismological Society of America. 99(4). 2335–2347. 14 indexed citations
13.
Delavaud, Élise, Frank Scherbaum, Nicolas Kuehn, & Carsten Riggelsen. (2009). Information-Theoretic Selection of Ground-Motion Prediction Equations for Seismic Hazard Analysis: An Applicability Study Using Californian Data. Bulletin of the Seismological Society of America. 99(6). 3248–3263. 83 indexed citations
14.
Köhler, Andreas, Matthias Ohrnberger, Carsten Riggelsen, & Frank Scherbaum. (2008). Unsupervised feature selection for pattern search in seismic time series. 106–120. 6 indexed citations
15.
Riggelsen, Carsten. (2008). Approximation Methods for Efficient Learning of Bayesian Networks. CERN Document Server (European Organization for Nuclear Research). 168(1). 1–137. 9 indexed citations
16.
Köhler, Andreas, Matthias Ohrnberger, Carsten Riggelsen, & Frank Scherbaum. (2008). Unsupervised Feature Selection for Pattern Discovery in Seismic Wavefields. 106–121. 1 indexed citations
17.
Riggelsen, Carsten. (2008). Learning Bayesian Networks: A MAP Criterion for Joint Selection of Model Structure and Parameter. 19. 522–529. 9 indexed citations
18.
19.
Riggelsen, Carsten & Ad Feelders. (2005). Learning Bayesian Network Models from Incomplete Data using Importance Sampling.. International Conference on Artificial Intelligence and Statistics. 15 indexed citations
20.
Riggelsen, Carsten. (2005). Learning parameters of Bayesian networks from incomplete data via importance sampling. International Journal of Approximate Reasoning. 42(1-2). 69–83. 24 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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