Prediction of Boundary and Stormwater
            <i>E. Coli</i>
            Concentrations Using River Flows and Baseflow Index

Jagupilla, Sarath Chandra K.; Shah, Vishwa; Ramaswamy, V.; Gurumurthy, P.; Vaccari, David A.

doi:10.1061/(asce)ee.1943-7870.0001681

Cited by 2 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the past, biological sludge and chaotic time-series [26] were modeled using MPR. TaylorFit is equipped to determine linear and nonlinear relationships between variables, including linear and nonlinear interactions among the predictors [24,27]. Such regression models have advantages over ANNs, as they are representational models, which enables them to be more easily analyzed using techniques such as sensitivity analysis using importance ratios and graphical approaches.…”

Section: Introductionmentioning

confidence: 99%

Non-Linear Visualization and Importance Ratio Analysis of Multivariate Polynomial Regression Ecological Models Based on River Hydromorphology and Water Quality

Shah

Jagupilla

Vaccari

et al. 2021

Water

Self Cite

View full text Add to dashboard Cite

Multivariate polynomial regression (MPR) models were developed for five macrophyte indices. MPR models are able to capture complex interactions in the data while being tractable and transparent for further analysis. The performance of the MPR modeling approach was compared to previous work using artificial neural networks. The data were obtained from hydromorphologically modified Polish rivers with a widely varying water quality. The modeled indices were the Macrophyte Index for Rivers (MIR), the Macrophyte Biological Index for Rivers (IBMR), and the River Macrophyte Nutrient Index (RMNI). These indices measure the trophic and ecological status of the rivers. Additionally, two biological diversity indices, species richness (N) and the Simpson index (D), were modeled. The explanatory variables were physico-chemical properties depicting water quality and river hydromorphological status indices. In comparison to artificial neural networks, the MPR models performed similarly in terms of goodness of fit. However, the MPR models had advantages such as model simplicity and ability to be subject to effective visualization of complex nonlinear input–output relationships, as well as facilitating sensitivity analysis using importance ratios to identify effects of individual input variables.

show abstract

Section: Introductionmentioning

confidence: 99%