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Title: A manifold learning perspective on surrogate modeling of nitrate concentration in the Kansas River

Journal Article · · Water Practice and Technology
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Abstract A non-linear surrogate model of nitrate concentration in the Kansas River (USA) is described. The model is an (almost) Piece-wise Linear response surface that provides a mean field approximation to the dynamics of the measured data for nitrate plus nitrite (target product) correlations to turbidity and chlorophyll-a concentrations (input variables). The method extends the United States Geological Survey’s linear procedures for surrogate data modeling allowing for better approximations for river systems exhibiting algal blooms due to nutrient-rich source waters. The model and visualization procedures illustrated in the Kansas River example should be generally applicable to many medium-size rivers in agricultural regions.

Sponsoring Organization:
USDOE
Grant/Contract Number:
SC0020843
OSTI ID:
2326264
Journal Information:
Water Practice and Technology, Journal Name: Water Practice and Technology Journal Issue: 4 Vol. 19; ISSN 1751-231X
Publisher:
IWA PublishingCopyright Statement
Country of Publication:
United Kingdom
Language:
English

References (10)

Estimation of constituent concentrations, densities, loads, and yields in lower Kansas River, northeast Kansas, using regression models and continuous water-quality monitoring, January 2000 through December 2003 report January 2005
Linear Regression Analysis book January 2003
Logistic and linear regression model documentation for statistical relations between continuous real-time and discrete water-quality constituents in the Kansas River, Kansas, July 2012 through June 2015 report January 2016
A nonlinear autoregressive exogenous (NARX) model to predict nitrate concentration in rivers journal January 2022
Guidelines and Procedures for Computing Time-Series Suspended-Sediment Concentrations and Loads from In-Stream Turbidity-Sensor and Streamflow Data report January 2009
Linking optical data and nitrates in the Lower Mississippi River to enable satellite‐based monitoring of nutrient reduction goals journal February 2024
Linear regression model documentation and updates for computing water-quality constituent concentrations or densities using continuous real-time water-quality data for the Kansas River, Kansas, July 2012 through September 2019 report January 2021
A simple metric for predicting the timing of river phytoplankton blooms journal December 2022
Estimation of nonlinear water-quality trends in high-frequency monitoring data journal May 2020
Monitoring the riverine pulse: Applying high‐frequency nitrate data to advance integrative understanding of biogeochemical and hydrological processes journal April 2019

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