Vishwa Shah
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Vishwa Shah has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Sarath Chandra Kumar Jagupilla
Department of Civil, Ocean and Environmental Engineering, Stevens Institute of Technology, Castle Point on Hudson, Hoboken, NJ 07030, USA
2 shared publications - Daniel Gebler
Department of Ecology and Environmental Protection, Poznan University of Life Sciences, Poznań, Poland
1 shared publication David A. Vaccari
Professor, Dept. of Civil, Environmental, and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07030
1 shared publication- David A. Vaccari
Professor, Dept. of Civil, Environmental, and Ocean Engineering, Stevens Institute of Technology, Castle Point on Hudson, Hoboken, NJ 07030. ORCID
1 shared publication - Praneeth Gurumurthy
Ph.D. Student, MIT-WHOI Joint Program, Massachusetts Institute of Technology, Woods Hole, MA 02543. ORCID
1 shared publication Venkatsundar Ramaswamy
Hydrologist, Jupiter Intelligence, 251 W 30 St., New York, NY 10001
1 shared publication
Latest Publications
- Open Access
Non-Linear Visualization and Importance Ratio Analysis of Multivariate Polynomial Regression Ecological Models Based on River Hydromorphology and Water Quality
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.
Open AccessNon-Linear Visualization and Importance Ratio Analysis of Multivariate Polynomial Regression Ecological Models Based on River Hydromorphology and Water Quality
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.
- 1 April 2020
- Published by American Society of Civil Engineers (ASCE) in Journal of Environmental Engineering
- Vol. 146 (4)
Prediction of Boundary and Stormwater E. Coli Concentrations Using River Flows and Baseflow Index
E. coli (EC) concentrations of the upstream boundary, tributaries, and stormwater in the lower Passaic River at Paterson, New Jersey, were modeled using multivariate polynomial regression (MPR). Baseflow indexes (BFIs) and river flows from upstream and downstream boundaries of the study area were used as predictors. The MPR models were developed by stepwise addition of the candidate terms. The candidate terms were selected based on their -statistics and the final term was selected based on the Nash-Sutcliffe efficiency (NSE) of the overall model. The NSE values of the models ranged from 0.61 to 0.88. The boundary concentrations were earlier modeled using symbolic regression without BFI as a predictor, resulting in a set of highly complex models for the same data. This study demonstrates the suitability of BFI as a water quality predictor and the importance of identifying suitable predictors to develop defensible empirical water quality models. Further, the relation between EC concentrations and BFI could be used to infer whether the predominant pollutant source at a location is independent of rainfall or is rainfall driven.- 1 April 2020
- Published by American Society of Civil Engineers (ASCE) in Journal of Environmental Engineering
- Vol. 146 (4)
Prediction of Boundary and Stormwater E. Coli Concentrations Using River Flows and Baseflow Index
E. coli (EC) concentrations of the upstream boundary, tributaries, and stormwater in the lower Passaic River at Paterson, New Jersey, were modeled using multivariate polynomial regression (MPR). Baseflow indexes (BFIs) and river flows from upstream and downstream boundaries of the study area were used as predictors. The MPR models were developed by stepwise addition of the candidate terms. The candidate terms were selected based on their -statistics and the final term was selected based on the Nash-Sutcliffe efficiency (NSE) of the overall model. The NSE values of the models ranged from 0.61 to 0.88. The boundary concentrations were earlier modeled using symbolic regression without BFI as a predictor, resulting in a set of highly complex models for the same data. This study demonstrates the suitability of BFI as a water quality predictor and the importance of identifying suitable predictors to develop defensible empirical water quality models. Further, the relation between EC concentrations and BFI could be used to infer whether the predominant pollutant source at a location is independent of rainfall or is rainfall driven.