2015
DOI: 10.1016/j.jhydrol.2015.03.010
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Event-based soil loss models for construction sites

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Cited by 24 publications
(18 citation statements)
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“…This equation demonstrates the exponential relationship between increased rainfall intensity and soil loss. Trenouth and Gharabaghi (2015), Liu et al (2015) and Thompson et al (2016) developed new event-based soil loss models that accurately predicted the adverse effects of extreme rainfall events on catchment surface runoff water quality. Stang et al (2016) investigated sediment rating curves using historic streamflow and water quality records in several basins in Ontario and clearly demonstrated that suspended sediment concentrations and turbidity levels increase by orders of magnitude during heavy storm events ( Figure 1).…”
Section: Relationship Between Water Turbidity and Heavy Storms-soil Ementioning
confidence: 99%
“…This equation demonstrates the exponential relationship between increased rainfall intensity and soil loss. Trenouth and Gharabaghi (2015), Liu et al (2015) and Thompson et al (2016) developed new event-based soil loss models that accurately predicted the adverse effects of extreme rainfall events on catchment surface runoff water quality. Stang et al (2016) investigated sediment rating curves using historic streamflow and water quality records in several basins in Ontario and clearly demonstrated that suspended sediment concentrations and turbidity levels increase by orders of magnitude during heavy storm events ( Figure 1).…”
Section: Relationship Between Water Turbidity and Heavy Storms-soil Ementioning
confidence: 99%
“…This complexity necessitates the use of novel mathematical tools which have the flexibility to model the full range of pollutant loadings and complex antecedent conditions, particularly when these processes are non-linear and difficult to describe mechanistically. Artificial neural networks using multiple nonlinear regression techniques lend themselves to simulating such processes, and will be evaluated for their ability to predict pollutant EMCs and MDUALs (Oreskes et al, 1994;Trenouth and Gharabaghi, 2015b).…”
Section: Seasonal Effects (Season)mentioning
confidence: 99%
“…As noted by Schmueli (2010), there are many instances where the accurate prediction Thompson et al (1996). of such phenomena is a far more pressing and important issue than explaining their behaviour. In such cases the predictive capability of artificial neural networks is hard to ignore (Trenouth and Gharabaghi, 2015b). Statistical evaluation of the various ANN models performance metrics suggest that the TSS EMC model performed better when seasonal variability was considered within the model.…”
Section: Ann Model Performancementioning
confidence: 99%
“…This paper provides additional discussion surrounding the novel event-based soil loss models developed by Trenouth and Gharabaghi (2015) for the design of erosion and sediment controls (ESCs) for various phases of construction -from pre-development to post-development conditions. The datasets for the study were obtained from three Ontario sites -Greensborough, Cookstown, and Alcona -in addition to datasets mined from the literature for three additional sites -Treynor, Iowa, Coshocton, Ohio and Cordoba, Spain.…”
Section: S U M M a R Ymentioning
confidence: 99%
“…The event-based ANN model presented by Trenouth and Gharabaghi (2015) fits within a broader conceptual framework which seeks to address such needs (Fig. 3).…”
Section: Utility Of a Predictive Design Toolmentioning
confidence: 99%