Andreas Wünsch

2.4k citations
55 papers · 1.3k indexed · 1 hit paper · h-index 17

Andreas Wünsch

47 papers receiving 1.3k citations

Hit Papers

Groundwater level forecasting with artificial neural netw...199202120262022202450100150

Peers

Andreas Wünsch
Comparison fields: 5 of 134
  • Transplantation 199
  • Environmental Engineering 440
  • Water Science and Technology 386
  • Geochemistry and Petrology 72
  • Global and Planetary Change 244
Replace Carlos Alberto Nunes Cosenza with:
Carlos Alberto Nunes Cosenza Brazil
Ikuo Takeda Japan
Wolfgang Schmid United States
Florian Wagner Germany
Qi Jiang China
Akira Miyata Japan
J. Luis Guerrero United States
Zoltán Máthé Hungary
Shintaro Yagi Japan
Andreas Wünsch relative to Carlos Alberto Nunes Cosenza Brazil Carlos Alberto Nunes Cosenza's profile →
Citations per field
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Carlos Alberto Nunes Cosenza · 1×
Citations per year

Countries citing papers authored by Andreas Wünsch

Since Specialization
Citations

This map shows the geographic impact of Andreas Wünsch'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 Andreas Wünsch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andreas Wünsch more than expected).

Fields of papers citing papers by Andreas Wünsch

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Andreas Wünsch. 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 Andreas Wünsch. The network helps show where Andreas Wünsch may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Andreas Wünsch, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Andreas Wünsch Line = papers co-authored together Andreas Wünsch links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20241
2 20240
3 20244
4 20231
5 20238
6 202210
7 2022131
8 202241
9 20210
10
Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX)breakdown →
2021199
11 20214
12 201812
13 20172
14 20130
15 200930
16 20096
17 200828
18 20077
19 20022
20 199815

About Andreas Wünsch

Andreas Wünsch is a scholar working on Transplantation, Environmental Engineering and Water Science and Technology, having authored 55 papers that have together received 1.3k indexed citations. Recurring topics across this work include Renal Transplantation Outcomes and Treatments (19 papers), Organ Transplantation Techniques and Outcomes (18 papers), Hydrology and Watershed Management Studies (12 papers), Hydrological Forecasting Using AI (9 papers), Organ Donation and Transplantation (8 papers), Liver Disease and Transplantation (5 papers), Karst Systems and Hydrogeology (4 papers) and Transplantation: Methods and Outcomes (3 papers). The work is most often cited by research in Transplantation (199 citations), Environmental Engineering (440 citations) and Water Science and Technology (386 citations). Andreas Wünsch has collaborated with scholars based in Germany, Slovenia and France. Frequent co-authors include Tanja Liesch, Stefan Broda, Richard Viebahn, Peter Schenker, W. Kübler, Christoph Bode, Jens R. Allenberg, Peter P. Nawroth, Christian Conradt and Michael Boehme. Their work appears in journals such as Hydrology and earth system sciences, Transplantation, Journal of Hydrology, Transplant International and Water Resources Management.

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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