Michael Siek
Impact in
- Management Information Systems top 10%
- FinTech, Crowdfunding, Digital Finance
- Business Process Modeling and Analysis
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- Hydrological Forecasting Using AI
Papers in
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- Business Process Modeling and Analysis 3
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- Neural Networks and Applications 3
- Co-authors
- DIMITRI P. SOLOMATINE (8 shared papers)D. P. Solomatine (1 shared paper)Slavco Velickov (2 shared papers)Varunesh Chandra (1 shared paper)
- Journals
- Neural Networks (1 paper)Nonlinear processes in geophysics (1 paper)IOP Conference Series Materials Science and Engineering (1 paper)IOP Conference Series Earth and Environmental Science (3 papers)EGU General Assembly Conference Abstracts (1 paper)
- Partner nations
- IndonesiaNetherlandsGermany
In The Last Decade
Michael Siek
31 papers receiving 279 citations
Peers
Comparison fields: 5 of 63
- Management Information Systems 66
- Environmental Engineering 65
- Water Science and Technology 49
- Information Systems and Management 24
- Information Systems 55
Countries citing papers authored by Michael Siek
This map shows the geographic impact of Michael Siek'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 Michael Siek with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Siek more than expected).
Fields of papers citing papers by Michael Siek
This network shows the impact of papers produced by Michael Siek. 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 Michael Siek. The network helps show where Michael Siek may publish in the future.
Co-authors
The 4 scholars most cited alongside Michael Siek, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 34 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 63 | |
| 2 | 2006 | 50 | |
| 3 | 2004 | 34 | |
| 4 | 2010 | 27 | |
| 5 | 2021 | 17 | |
| 6 | 2011 | 11 | |
| 7 | 2020 | 9 | |
| 8 | 2021 | 8 | |
| 9 | 2005 | 8 | |
| 10 | 2022 | 7 | |
| 11 | 2023 | 7 | |
| 12 | 2021 | 5 | |
| 13 | 2010 | 5 | |
| 14 | 2021 | 5 | |
| 15 | 2008 | 5 | |
| 16 | OPTIMIZING MIXTURES OF LOCAL EXPERTS IN TREE-LIKE REGRESSION MODELS | 2005 | 4 |
| 17 | 2009 | 4 | |
| 18 | 2011 | 4 | |
| 19 | 2021 | 4 | |
| 20 | 2023 | 3 |
About Michael Siek
Michael Siek is a scholar working on Management Information Systems, Artificial Intelligence, Atmospheric Science, Information Systems and Environmental Engineering, having authored 34 papers that have together received 305 indexed citations. Recurring topics across this work include Hydrological Forecasting Using AI (5 papers), Meteorological Phenomena and Simulations (5 papers), Tropical and Extratropical Cyclones Research (4 papers), Stock Market Forecasting Methods (3 papers), Neural Networks and Applications (3 papers), Business Process Modeling and Analysis (3 papers), Manufacturing Process and Optimization (2 papers) and Chaos control and synchronization (2 papers). The work is most often cited by research in Management Information Systems (66 citations), Environmental Engineering (65 citations), Water Science and Technology (49 citations), Information Systems and Management (24 citations) and Information Systems (55 citations). Michael Siek has collaborated with scholars based in Indonesia, Netherlands and Germany. Frequent co-authors include DIMITRI P. SOLOMATINE, D. P. Solomatine, Slavco Velickov and Varunesh Chandra. Their work appears in journals such as Neural Networks, Nonlinear processes in geophysics, IOP Conference Series Materials Science and Engineering, IOP Conference Series Earth and Environmental Science and EGU General Assembly Conference Abstracts.
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.