A. W. Minns

1.5k total citations · 1 hit paper
10 papers, 859 citations indexed

About

A. W. Minns is a scholar working on Environmental Engineering, Water Science and Technology and Global and Planetary Change. According to data from OpenAlex, A. W. Minns has authored 10 papers receiving a total of 859 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Environmental Engineering, 5 papers in Water Science and Technology and 4 papers in Global and Planetary Change. Recurrent topics in A. W. Minns's work include Hydrological Forecasting Using AI (5 papers), Hydrology and Watershed Management Studies (5 papers) and Hydrology and Drought Analysis (3 papers). A. W. Minns is often cited by papers focused on Hydrological Forecasting Using AI (5 papers), Hydrology and Watershed Management Studies (5 papers) and Hydrology and Drought Analysis (3 papers). A. W. Minns collaborates with scholars based in Netherlands and Australia. A. W. Minns's co-authors include Michael J. Hall, Arthur E. Mynett, Michael B. Abbott, Yonas Dibike, Dimitri Solomatine, M. B. Abbott and Adrianus Verwey and has published in prestigious journals such as Ecological Modelling, Hydrology and earth system sciences and Hydrological Sciences Journal.

In The Last Decade

A. W. Minns

10 papers receiving 789 citations

Hit Papers

align trajectories

What are hit papers?

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.

Artificial neural networks as rainfall-runoff models

1996 591 citations A. W. Minns, Michael J. Hall Hydrological Sciences Journal profile →

Peers

A. W. Minns

EE

WST

GPC

EEE

AI

U.S. Panu Canada

T.M.K.G. Fernando Australia

Lu‐Hsien Chen Taiwan

Bagher Shirmohammadi Iran

Yen‐Ming Chiang Taiwan

Mustafa Tombul Türkiye

Claire Brenner Austria

Vahid Karimi Iran

Yiqing Guan China

U.S. Panu Canada

A. W. Minns

434 ×0.7

EE

547 ×0.9

WST

761 ×1.7

GPC

73 ×0.8

EEE

113 ×1.4

AI

Citations per year, relative to A. W. Minns A. W. Minns (= 1×) peers U.S. Panu

Countries citing papers authored by A. W. Minns

Since Specialization

Citations

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

Fields of papers citing papers by A. W. Minns

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by A. W. Minns. 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 A. W. Minns. The network helps show where A. W. Minns may publish in the future.

Co-authorship network of co-authors of A. W. Minns

This figure shows the co-authorship network connecting the top 25 collaborators of A. W. Minns. A scholar is included among the top collaborators of A. W. Minns 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 A. W. Minns. A. W. Minns is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

1.

Abbott, Michael B. & A. W. Minns. (2017). The method of characteristics. 123–204. 1 indexed citations

2.

Hall, Michael J., et al.. (2005). The extrapolation of artificial neural networks for the modelling of rainfall—runoff relationships. Journal of Hydroinformatics. 7(4). 291–296. 47 indexed citations

3.

Hall, Michael J., et al.. (2002). The application of data mining techniques for the regionalisation of hydrological variables. Hydrology and earth system sciences. 6(4). 685–694. 28 indexed citations

4.

Mynett, Arthur E., et al.. (2002). Application of cellular automata to modelling competitive growths of two underwater species Chara aspera and Potamogeton pectinatus in Lake Veluwe. Ecological Modelling. 147(3). 253–265. 36 indexed citations

5.

Minns, A. W.. (2000). Subsymbolic methods for data mining in hydraulic engineering. Journal of Hydroinformatics. 2(1). 3–13. 16 indexed citations

6.

Dibike, Yonas, A. W. Minns, & Michael B. Abbott. (1999). Applications of artificial neural networks to the generation of wave equations from hydraulic data. Journal of Hydraulic Research. 37(1). 81–97. 30 indexed citations

7.

Hall, Michael J. & A. W. Minns. (1999). The classification of hydrologically homogeneous regions. Hydrological Sciences Journal. 44(5). 693–704. 105 indexed citations

8.

Minns, A. W.. (1998). Artificial neural networks as subsymbolic process descriptors : dissertation, submitted in fulfilment of the requirements of the Board for Doctorates of Delft University of Technology and the Academic Board of the International Institute for Infrastructural, Hydraulic and Environmental Engineering for the Degree of Doctor to be defended in public on Monday, 2 March 1998 at 13:30 h. A.A. Balkema eBooks. 2 indexed citations

9.

Minns, A. W. & Michael J. Hall. (1996). Artificial neural networks as rainfall-runoff models. Hydrological Sciences Journal. 41(3). 399–417. 591 indexed citations breakdown →

10.

Abbott, M. B., et al.. (1994). Education and training in hydroinformatics. Journal of Hydraulic Research. 32(sup1). 203–214. 3 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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