Abstract
Symbolic regression was used to model E. coli concentrations of upstream boundary, tributaries, and stormwater in the lower Passaic River at Paterson, New Jersey. These models were used to simulate boundary concentrations for a water quality analysis simulation program to model the river. River flows from upstream and downstream boundaries of the study area were used as predictors. The symbolic regression technique developed a variety of candidate models to choose from due to multiple transformations and model structures considered. The resulting models had advantages such as better goodness-of-fit statistics, reasonable bounds to outputs, and smooth behavior. The major disadvantages of the technique are model complexity, difficulty to interpret, and overfitting. The Nash-Sutcliffe efficiencies of the models ranged from 0.61 to 0.88, and they adequately captured the upstream boundary, tributary, and stormwater concentrations. The results suggest symbolic regression can have significant applications in the areas of hydrologic, hydrodynamic, and water quality modeling.
| Original language | English |
|---|---|
| Pages (from-to) | 26-34 |
| Number of pages | 9 |
| Journal | Water Environment Research |
| Volume | 87 |
| Issue number | 1 |
| State | Published - 1 Jan 2015 |
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Jagupilla, S. C. H. K., Vaccari, D. A., Miskewitz, R., Su, T. L., & Hires, R. I. (2015). Symbolic regression of upstream, stormwater, and tributary E. coli concentrations using river flows. Water Environment Research, 87(1), 26-34.
Jagupilla, Sarath C.handra K. ; Vaccari, David A. ; Miskewitz, Robert et al. / Symbolic regression of upstream, stormwater, and tributary E. coli concentrations using river flows. In: Water Environment Research. 2015 ; Vol. 87, No. 1. pp. 26-34.
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title = "Symbolic regression of upstream, stormwater, and tributary E. coli concentrations using river flows",
abstract = "Symbolic regression was used to model E. coli concentrations of upstream boundary, tributaries, and stormwater in the lower Passaic River at Paterson, New Jersey. These models were used to simulate boundary concentrations for a water quality analysis simulation program to model the river. River flows from upstream and downstream boundaries of the study area were used as predictors. The symbolic regression technique developed a variety of candidate models to choose from due to multiple transformations and model structures considered. The resulting models had advantages such as better goodness-of-fit statistics, reasonable bounds to outputs, and smooth behavior. The major disadvantages of the technique are model complexity, difficulty to interpret, and overfitting. The Nash-Sutcliffe efficiencies of the models ranged from 0.61 to 0.88, and they adequately captured the upstream boundary, tributary, and stormwater concentrations. The results suggest symbolic regression can have significant applications in the areas of hydrologic, hydrodynamic, and water quality modeling.",
author = "Jagupilla, {Sarath C.handra K.} and Vaccari, {David A.} and Robert Miskewitz and Su, {Tsan Liang} and Hires, {Richard I.}",
year = "2015",
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Jagupilla, SCHK, Vaccari, DA, Miskewitz, R, Su, TL & Hires, RI 2015, 'Symbolic regression of upstream, stormwater, and tributary E. coli concentrations using river flows', Water Environment Research, vol. 87, no. 1, pp. 26-34.
Symbolic regression of upstream, stormwater, and tributary E. coli concentrations using river flows. / Jagupilla, Sarath C.handra K.; Vaccari, David A.; Miskewitz, Robert et al.
In: Water Environment Research, Vol. 87, No. 1, 01.01.2015, p. 26-34.
Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - Symbolic regression of upstream, stormwater, and tributary E. coli concentrations using river flows
AU - Jagupilla, Sarath C.handra K.
AU - Vaccari, David A.
AU - Miskewitz, Robert
AU - Su, Tsan Liang
AU - Hires, Richard I.
PY - 2015/1/1
Y1 - 2015/1/1
N2 - Symbolic regression was used to model E. coli concentrations of upstream boundary, tributaries, and stormwater in the lower Passaic River at Paterson, New Jersey. These models were used to simulate boundary concentrations for a water quality analysis simulation program to model the river. River flows from upstream and downstream boundaries of the study area were used as predictors. The symbolic regression technique developed a variety of candidate models to choose from due to multiple transformations and model structures considered. The resulting models had advantages such as better goodness-of-fit statistics, reasonable bounds to outputs, and smooth behavior. The major disadvantages of the technique are model complexity, difficulty to interpret, and overfitting. The Nash-Sutcliffe efficiencies of the models ranged from 0.61 to 0.88, and they adequately captured the upstream boundary, tributary, and stormwater concentrations. The results suggest symbolic regression can have significant applications in the areas of hydrologic, hydrodynamic, and water quality modeling.
AB - Symbolic regression was used to model E. coli concentrations of upstream boundary, tributaries, and stormwater in the lower Passaic River at Paterson, New Jersey. These models were used to simulate boundary concentrations for a water quality analysis simulation program to model the river. River flows from upstream and downstream boundaries of the study area were used as predictors. The symbolic regression technique developed a variety of candidate models to choose from due to multiple transformations and model structures considered. The resulting models had advantages such as better goodness-of-fit statistics, reasonable bounds to outputs, and smooth behavior. The major disadvantages of the technique are model complexity, difficulty to interpret, and overfitting. The Nash-Sutcliffe efficiencies of the models ranged from 0.61 to 0.88, and they adequately captured the upstream boundary, tributary, and stormwater concentrations. The results suggest symbolic regression can have significant applications in the areas of hydrologic, hydrodynamic, and water quality modeling.
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Jagupilla SCHK, Vaccari DA, Miskewitz R, Su TL, Hires RI. Symbolic regression of upstream, stormwater, and tributary E. coli concentrations using river flows. Water Environment Research. 2015 Jan 1;87(1):26-34.
