Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Model Induction with Support Vector Machines: Introduction and Applications
2001510 citationsDimitri Solomatine et al.profile →
Data-driven modelling: some past experiences and new approaches
2007502 citationsDimitri Solomatine et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Dimitri Solomatine
Since
Specialization
Citations
This map shows the geographic impact of Dimitri Solomatine'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 Dimitri Solomatine with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dimitri Solomatine more than expected).
Fields of papers citing papers by Dimitri Solomatine
This network shows the impact of papers produced by Dimitri Solomatine. 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 Dimitri Solomatine. The network helps show where Dimitri Solomatine may publish in the future.
Co-authorship network of co-authors of Dimitri Solomatine
This figure shows the co-authorship network connecting the top 25 collaborators of Dimitri Solomatine.
A scholar is included among the top collaborators of Dimitri Solomatine 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 Dimitri Solomatine. Dimitri Solomatine is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Lanen, H.A.J. van, et al.. (2018). Comparative analysis of two evaporation-based drought indicators for large-scale drought monitoring. EGU General Assembly Conference Abstracts. 18728.1 indexed citations
8.
Solomatine, Dimitri, et al.. (2017). Towards socio-hydroinformatics: optimal design and integration of citizen-based information in water-system models. EGUGA. 12370.1 indexed citations
9.
Alfonso, Leonardo, et al.. (2017). Dimensioning of precipitation citizen observatories in an uncertainty-aware context. EGU General Assembly Conference Abstracts. 18523.2 indexed citations
10.
Alfonso, Leonardo, et al.. (2016). Optimal design of hydrometric monitoring networks with dynamic components based on Information Theory. EGU General Assembly Conference Abstracts.2 indexed citations
11.
Mazzoleni, Maurizio, Leonardo Alfonso, & Dimitri Solomatine. (2015). Improving flood prediction by assimilation of the distributed streamflow observations with variable uncertainty and intermittent behavior. EGU General Assembly Conference Abstracts. 15856.1 indexed citations
12.
Corzo, Gerald & Dimitri Solomatine. (2014). Comparative analysis of conceptual models with error correction, artificial neural networks and committee models. EGUGA. 8898.2 indexed citations
13.
Solomatine, Dimitri, et al.. (2014). Applying clustering approach in predictive uncertainty estimation: a case study with the UNEEC method. EGUGA. 5992.2 indexed citations
14.
Solomatine, Dimitri, et al.. (2013). Combinations of specilaized conceptual and neural network rainfall-runoff models: comparison of performance. EGUGA.1 indexed citations
15.
Jonoski, Andréja, et al.. (2012). Cloud computing framework for a hydro information system. Goce Delchev University Repository (Goce Delčev University of Štip).1 indexed citations
16.
Pianosi, Francesca, Durga Lal Shrestha, & Dimitri Solomatine. (2010). Uncertainty analysis of an inflow forecasting model: extension of the UNEEC machine learning-based method. EGUGA. 12478.1 indexed citations
17.
Corzo, Gerald, et al.. (2009). Ensemble of radar and MM5 precipitation forecast models with M5 model trees.. IAHS-AISH publication. 14–19.2 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.