Árpád Csámer

25 papers receiving 441 citations

Hit Papers

Comparative assessment of machine learning models for landslide susceptibility mapping: a focus on validation and accuracy 2025 · 14 citations
140Years since publication4812

Peers

Árpád Csámer
Comparison fields: 5 of 51
  • Media Technology 203
  • Artificial Intelligence 352
  • Environmental Engineering 149
  • Geophysics 125
  • Mechanics of Materials 76
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Sabreen Gad United States
Enton Bedini Denmark
Mehdi Honarmand Iran
Safaa M. Hassan Egypt
Milad Sekandari Iran
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Mohammed Raji Morocco
Robert Jackisch Germany
Ehsan Farahbakhsh Australia
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Countries citing papers authored by Árpád Csámer

Since Specialization
Citations

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

Fields of papers citing papers by Árpád Csámer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 11 scholars most cited alongside Árpád Csámer, 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 Árpád Csámer Line = papers co-authored together Árpád Csámer links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 26 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202150
2 202144
3 202342
4 202142
5 202236
6 202234
7 202330
8 202130
9 202221
10 202419
11 202118
12 202216
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Comparative assessment of machine learning models for landslide susceptibility mapping: a focus on validation and accuracy
Hit paper breakdown →
202514
14 202411
15 20238
16 20236
17 20256
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20 20213

About Árpád Csámer

Árpád Csámer is a scholar working on Artificial Intelligence, Media Technology, Environmental Engineering, Global and Planetary Change and Mechanics of Materials, having authored 26 papers that have together received 449 indexed citations. Recurring topics across this work include Geochemistry and Geologic Mapping (19 papers), Remote-Sensing Image Classification (14 papers), Soil Geostatistics and Mapping (7 papers), Hydrocarbon exploration and reservoir analysis (4 papers), Groundwater and Watershed Analysis (4 papers), Mineral Processing and Grinding (4 papers), Geophysical and Geoelectrical Methods (3 papers) and Landslides and related hazards (3 papers). The work is most often cited by research in Media Technology (203 citations), Artificial Intelligence (352 citations), Environmental Engineering (149 citations), Geophysics (125 citations) and Mechanics of Materials (76 citations). Árpád Csámer has collaborated with scholars based in Hungary, Egypt and Japan. Frequent co-authors include Ali Shebl, Sayed O. Elkhateeb, Timothy Kusky, Yasushi Watanabe, Mohamed Abdelkader, Hosni Ghazala, Sultan Awad Sultan Araffa, Mohamed Abd El‐Wahed, Mahmoud M. El-Rahmany and Ferenc Kristály. Their work appears in journals such as Remote Sensing Applications Society and Environment, The Egyptian Journal of Remote Sensing and Space Science, Ore Geology Reviews, Scientific Reports and Geomatics Natural Hazards and Risk.

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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