Nonnegative tensor factorization for contaminant source identification

Vesselinov, Velimir V.; Alexandrov, Boian S.; O’Malley, Daniel

doi:10.1016/j.jconhyd.2018.11.010

Cited by 16 publications

(11 citation statements)

References 44 publications

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“…Additional details regarding the nonnegative CPD technique can be found in the work of Alexandrov et al. (2019), where it was used for the analysis of phase transition data, and in the work of Vesselinov, Alexandrov, and O'Malley (2019), where it was used to analyze data of groundwater contamination. In particular, the work of Alexandrov et al.…”

Section: Methodsmentioning

confidence: 99%

“…Colored areas represent the crop rotation and intensive sprinkling. Legend line labels refer to Cl − and N treatments and plot numbersVesselinov, Alexandrov, and O'Malley (2019), where it was used to analyze data of groundwater contamination. In particular, the work ofAlexandrov et al (2019) describes the technique to find the number of latent patterns in the data.…”

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confidence: 99%

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Increased irrigation water salinity enhances nitrate transport to deep unsaturated soil

Weissman

Bel

Ben‐Gal

et al. 2020

Vadose Zone Journal

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Excessive use of N fertilizers in agriculture often leads to NO 3 − accumulation in the unsaturated zone and to groundwater pollution. There is uncertainty regarding the variability in fertilizer transport and uptake efficiency due to the lack of studies based on continuous nondestructive measurements in unsaturated soils. In this study, we analyzed solute dynamics across the unsaturated zone underlying cultivated agricultural fields. Commercial crop rotations under four treatments, comprising two N fertilization regimes and two irrigation water salinity levels, were conducted in loess soil in the semiarid climate of the northern Negev Desert, Israel. The impact of the various treatments on water and solute dynamics below the root zone was monitored by a vadose zone monitoring system. The patterns of variations in soil water content and solute concentrations were analyzed using nonnegative tensor factorization. We found that irrigating using higher salinity water resulted in the earlier arrival of wetting fronts to the deeper layers and increased NO 3 − concentrations relative to the lower salinity treatments. Surprisingly, this effect was only seen in the deeper soil levels, whereas there was no significant difference in the arrival times and concentrations in the upper soil layers. Possible mechanisms are suggested and discussed. Abbreviations: CPD, canonical polyadic decomposition; FTDR, flexible time domain reflectometer; PSD, particle size distribution; VMS, vadose zone monitoring system; VSP, vadose zone pore water sampling port. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Methodsmentioning

confidence: 99%

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confidence: 99%

Increased irrigation water salinity enhances nitrate transport to deep unsaturated soil

Weissman

Bel

Ben‐Gal

et al. 2020

Vadose Zone Journal

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“…The applied unsupervised machine learning based on non‐negative matrix factorization (NMFk) is open source and a part of a general AI/ML framework called SmartTensors. The source code, documentation, examples, and results from other ML studies are available at https://github.com/SmartTensors (Accessed 30 March 2022; Vesselinov et al., 2019).…”

Section: Data Availability Statementmentioning

confidence: 99%

Characterizing Drought Behavior in the Colorado River Basin Using Unsupervised Machine Learning

Talsma

Bennett

Vesselinov

2022

Earth and Space Science

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“…However, inverse tracking of the contaminant source is a problem, due to the lack of observed data and complexity of the mixing processes in a natural river. In order to overcome this limitation, a number of methods for the identification of contaminant sources have been suggested, mainly in the groundwater system; these use various techniques, such as optimization, geostatistical simulations, analytical solutions, and data-driven models [4][5][6][7][8][9][10][11][12][13][14][15]. Although contaminant source identification problems in both rivers and groundwater have a similar purpose, applying the methods developed for groundwater to rivers is challenging, due to the difference in flow and mixing characteristics between groundwater and rivers.…”

Section: Introductionmentioning

confidence: 99%

Identification Framework of Contaminant Spill in Rivers Using Machine Learning with Breakthrough Curve Analysis

Kwon

Noh

Seo

et al. 2021

IJERPH

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To minimize the damage from contaminant accidents in rivers, early identification of the contaminant source is crucial. Thus, in this study, a framework combining Machine Learning (ML) and the Transient Storage zone Model (TSM) was developed to predict the spill location and mass of a contaminant source. The TSM model was employed to simulate non-Fickian Breakthrough Curves (BTCs), which entails relevant information of the contaminant source. Then, the ML models were used to identify the BTC features, characterized by 21 variables, to predict the spill location and mass. The proposed framework was applied to the Gam Creek, South Korea, in which two tracer tests were conducted. In this study, six ML methods were applied for the prediction of spill location and mass, while the most relevant BTC features were selected by Recursive Feature Elimination Cross-Validation (RFECV). Model applications to field data showed that the ensemble Decision tree models, Random Forest (RF) and Xgboost (XGB), were the most efficient and feasible in predicting the contaminant source.

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Nonnegative tensor factorization for contaminant source identification

Cited by 16 publications

References 44 publications

Increased irrigation water salinity enhances nitrate transport to deep unsaturated soil

Increased irrigation water salinity enhances nitrate transport to deep unsaturated soil

Characterizing Drought Behavior in the Colorado River Basin Using Unsupervised Machine Learning

Identification Framework of Contaminant Spill in Rivers Using Machine Learning with Breakthrough Curve Analysis

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