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A Data-Efficient Approach for Monito...

Bin Mamoon, Wasif,

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A Data-Efficient Approach for Monitoring and Modeling Stream Nutrients /

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Data-Efficient Approach for Monitoring and Modeling Stream Nutrients // Wasif Bin Mamoon.
Author:	Bin Mamoon, Wasif,
Description:	1 electronic resource (186 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 87-05, Section: B.
Contained By:	Dissertations Abstracts International87-05B.
Subject:	Hydrologic sciences. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32279392
ISBN:	9798297964365

A Data-Efficient Approach for Monitoring and Modeling Stream Nutrients /
Bin Mamoon, Wasif,

A Data-Efficient Approach for Monitoring and Modeling Stream Nutrients /Wasif Bin Mamoon. - 1 electronic resource (186 pages)

Source: Dissertations Abstracts International, Volume: 87-05, Section: B.

Streams and rivers with excessive nutrients can compromise aquatic ecosystems, human health, and overall water resources. Precise, high-resolution measurements of nutrient concentrations are essential for pollution management programs; however, conventional monitoring methods frequently face limitations due to cost and sample restrictions. This dissertation aimed to advance the existing state-of-the-science nutrient monitoring and modeling approaches to reduce sampling requirements and model complexity.First, this study estimated the dimensionality and predictability of nutrient signals (NOx and TP signals), that describe the nutrient dynamics. The findings showed that nutrient data can be forecasted accurately for 1 to 5.5 months. Most of the signals displayed low to moderate dimensionality (5-10) and had some hidden order within their dynamics. This enabled the option of using reduced-order models to estimate concentration data.Second, this study implemented a data-driven sparse sensing (DSS) framework to estimate high-frequency nutrient data from minimal measurements. The simplest DSS models (training data consisting of only flow and nutrients) were able to estimate accurate daily concentrations and annual loads using as few as 40 samples per year (errors <±2% for NOx, <±9% for TP). Instead of relying on fixed-frequency sampling or the "storm chasing" approach, DSS models identified the optimal sampling times that contained the maximum information across the monitoring period.Finally, the DSS modeling framework was tested under real-world monitoring scenarios to address key challenges in designing nutrient monitoring programs. Results showed that accurate reconstructions (NSE > 0.5; load errors <±4%) are possible with only 2-3 years of data from 15 to 25 sites, especially when training data includes both flow and concentration.Overall, the results backed the use of reduced-order modeling approaches (i.e., DSS) that take advantage of the fact that nutrient signals are low-dimensional and sparse. Using DSS, daily nutrient concentration time-series can be generated with 80-90% reduction in the number of samples required, leading to a significant reduction in monitoring effort. These findings can support ongoing water quality management efforts by promoting monitoring strategies that are more resource-efficient and modeling frameworks that can operate with fewer parameters and limited data.

English

ISBN: 9798297964365Subjects--Topical Terms:

3168407
Hydrologic sciences.
Subjects--Index Terms:

Dimension reduction

A Data-Efficient Approach for Monitoring and Modeling Stream Nutrients /
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500 $a Source: Dissertations Abstracts International, Volume: 87-05, Section: B.
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520 $a Streams and rivers with excessive nutrients can compromise aquatic ecosystems, human health, and overall water resources. Precise, high-resolution measurements of nutrient concentrations are essential for pollution management programs; however, conventional monitoring methods frequently face limitations due to cost and sample restrictions. This dissertation aimed to advance the existing state-of-the-science nutrient monitoring and modeling approaches to reduce sampling requirements and model complexity.First, this study estimated the dimensionality and predictability of nutrient signals (NOx and TP signals), that describe the nutrient dynamics. The findings showed that nutrient data can be forecasted accurately for 1 to 5.5 months. Most of the signals displayed low to moderate dimensionality (5-10) and had some hidden order within their dynamics. This enabled the option of using reduced-order models to estimate concentration data.Second, this study implemented a data-driven sparse sensing (DSS) framework to estimate high-frequency nutrient data from minimal measurements. The simplest DSS models (training data consisting of only flow and nutrients) were able to estimate accurate daily concentrations and annual loads using as few as 40 samples per year (errors <±2% for NOx, <±9% for TP). Instead of relying on fixed-frequency sampling or the "storm chasing" approach, DSS models identified the optimal sampling times that contained the maximum information across the monitoring period.Finally, the DSS modeling framework was tested under real-world monitoring scenarios to address key challenges in designing nutrient monitoring programs. Results showed that accurate reconstructions (NSE > 0.5; load errors <±4%) are possible with only 2-3 years of data from 15 to 25 sites, especially when training data includes both flow and concentration.Overall, the results backed the use of reduced-order modeling approaches (i.e., DSS) that take advantage of the fact that nutrient signals are low-dimensional and sparse. Using DSS, daily nutrient concentration time-series can be generated with 80-90% reduction in the number of samples required, leading to a significant reduction in monitoring effort. These findings can support ongoing water quality management efforts by promoting monitoring strategies that are more resource-efficient and modeling frameworks that can operate with fewer parameters and limited data.
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653 $a Dimension reduction
653 $a Hydrologic time-series
653 $a Nonlinear dynamics
653 $a Sparse sensing
653 $a Stream nutrient monitoring and modeling
653 $a Stream signals
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710 2 $a Marquette University. $b Civil Engineering. $e degree granting institution. $3 3829347
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