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.
Neural Networks for River Flow Prediction
1994546 citationsWilliam J. Grenney et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by William J. Grenney
Since
Specialization
Citations
This map shows the geographic impact of William J. Grenney'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 William J. Grenney with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites William J. Grenney more than expected).
Fields of papers citing papers by William J. Grenney
This network shows the impact of papers produced by William J. Grenney. 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 William J. Grenney. The network helps show where William J. Grenney may publish in the future.
Co-authorship network of co-authors of William J. Grenney
This figure shows the co-authorship network connecting the top 25 collaborators of William J. Grenney.
A scholar is included among the top collaborators of William J. Grenney 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 William J. Grenney. William J. Grenney is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Grenney, William J., et al.. (2004). SURVEY OF IMPLEMENTATION STRATEGIES BY RURAL PARATRANSIT AGENCIES USING LOW-COST SOFTWARE.
Sims, Ronald C., et al.. (1988). Fate and Transport of Organics in Soil: Model Predictions and Experimental Results. Utah State Research and Scholarship (Utah State University). 60(9). 1684–1693.13 indexed citations
Grenney, William J., et al.. (1987). Development of knowledge-based expert systems to aid in hazardous waste management.2 indexed citations
10.
Sims, Ronald C., et al.. (1986). A Demonstration Expert System to Aid in Assessing Groundwater Contaminating Potential by Organic Chemicals. Computing in Civil Engineering. 687–698.1 indexed citations
11.
Grenney, William J., et al.. (1986). Expert System Tools for Civil Engineering Applications. 18–29.6 indexed citations
Malone, Ronald F., David S. Bowles, William J. Grenney, & Michael P. Windham. (1979). Stochastic Analysis for Water Quality. The Medical Journal of Australia. 2(3). 73–4.6 indexed citations
15.
Grenney, William J., et al.. (1977). Goal Programming Model for Water Quality Planning. Journal of Environmental Management. 103. 293.
16.
Grenney, William J., et al.. (1976). Capability of Integer Programming Algorithms in Solving Water Resource Planning Problems. Digital Commons - USU (Utah State University).2 indexed citations
17.
Grenney, William J., et al.. (1976). Development of a Water Quality Simulation Model Applicable to Great Salt Lake, Utah. Digital Commons - USU (Utah State University). 1.2 indexed citations
18.
Grenney, William J. & David S. Bowles. (1976). Estimating Flow Conditions for River Models. Journal of the Environmental Engineering Division. 102(4). 693–707.1 indexed citations
19.
Porcella, Donald B., et al.. (1975). Nutrient dynamics and gas production in aquatic ecosystems: The effects and utilization of mercury and nitrogen in sediment-water microcosms. Digital Commons - USU (Utah State University). 76. 33681.6 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.