Contaminant source identification using semi-supervised machine learning
Abstract
Identification of the original groundwater types present in geochemical mixtures observed in an aquifer is a challenging but very important task. Frequently, some of the groundwater types are related to different infiltration and/or contamination sources associated with various geochemical signatures and origins. The characterization of groundwater mixing processes typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical types. Numerous different geochemical constituents and processes may need to be simulated in these models which further complicates the analyses. In this paper, we propose a new contaminant source identification approach that performs decomposition of the observation mixtures based on Non-negative Matrix Factorization (NMF) method for Blind Source Separation (BSS), coupled with a custom semi-supervised clustering algorithm. Our methodology, called NMFk, is capable of identifying (a) the unknown number of groundwater types and (b) the original geochemical concentration of the contaminant sources from measured geochemical mixtures with unknown mixing ratios without any additional site information. NMFk is tested on synthetic and real-world site data. Finally, the NMFk algorithm worksmore »
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE Office of Environmental Management (EM)
- OSTI Identifier:
- 1408837
- Alternate Identifier(s):
- OSTI ID: 1526792
- Report Number(s):
- LA-UR-17-23269
Journal ID: ISSN 0169-7722; TRN: US1703077
- Grant/Contract Number:
- AC52-06NA25396; 11145687
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Contaminant Hydrology
- Additional Journal Information:
- Journal Volume: 212; Journal ID: ISSN 0169-7722
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; 97 MATHEMATICS AND COMPUTING; Earth Sciences; Mathematics; Non-negative matrix factorization; Feature Extraction; Blind Source Separation; Robustness analysis; Semi-supervised learning; Groundwater contamination; Source identification; Advection-diffusion transport; Geochemical signatures
Citation Formats
Vesselinov, Velimir Valentinov, Alexandrov, Boian S., and O’Malley, Dan. Contaminant source identification using semi-supervised machine learning. United States: N. p., 2017.
Web. doi:10.1016/j.jconhyd.2017.11.002.
Vesselinov, Velimir Valentinov, Alexandrov, Boian S., & O’Malley, Dan. Contaminant source identification using semi-supervised machine learning. United States. https://doi.org/10.1016/j.jconhyd.2017.11.002
Vesselinov, Velimir Valentinov, Alexandrov, Boian S., and O’Malley, Dan. Wed .
"Contaminant source identification using semi-supervised machine learning". United States. https://doi.org/10.1016/j.jconhyd.2017.11.002. https://www.osti.gov/servlets/purl/1408837.
@article{osti_1408837,
title = {Contaminant source identification using semi-supervised machine learning},
author = {Vesselinov, Velimir Valentinov and Alexandrov, Boian S. and O’Malley, Dan},
abstractNote = {Identification of the original groundwater types present in geochemical mixtures observed in an aquifer is a challenging but very important task. Frequently, some of the groundwater types are related to different infiltration and/or contamination sources associated with various geochemical signatures and origins. The characterization of groundwater mixing processes typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical types. Numerous different geochemical constituents and processes may need to be simulated in these models which further complicates the analyses. In this paper, we propose a new contaminant source identification approach that performs decomposition of the observation mixtures based on Non-negative Matrix Factorization (NMF) method for Blind Source Separation (BSS), coupled with a custom semi-supervised clustering algorithm. Our methodology, called NMFk, is capable of identifying (a) the unknown number of groundwater types and (b) the original geochemical concentration of the contaminant sources from measured geochemical mixtures with unknown mixing ratios without any additional site information. NMFk is tested on synthetic and real-world site data. Finally, the NMFk algorithm works with geochemical data represented in the form of concentrations, ratios (of two constituents; for example, isotope ratios), and delta notations (standard normalized stable isotope ratios).},
doi = {10.1016/j.jconhyd.2017.11.002},
journal = {Journal of Contaminant Hydrology},
number = ,
volume = 212,
place = {United States},
year = {Wed Nov 08 00:00:00 EST 2017},
month = {Wed Nov 08 00:00:00 EST 2017}
}
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Works referenced in this record:
Blind source separation for groundwater pressure analysis based on nonnegative matrix factorization
journal, September 2014
- Alexandrov, Boian S.; Vesselinov, Velimir V.
- Water Resources Research, Vol. 50, Issue 9
Deciphering Signatures of Mutational Processes Operative in Human Cancer
journal, January 2013
- Alexandrov, Ludmil B.; Nik-Zainal, Serena; Wedge, David C.
- Cell Reports, Vol. 3, Issue 1
Pollution source identification in heterogeneous porous media
journal, August 2001
- Atmadja, Juliana; Bagtzoglou, Amvrossios C.
- Water Resources Research, Vol. 37, Issue 8
A blind source separation technique using second-order statistics
journal, January 1997
- Belouchrani, A.; Abed-Meraim, K.; Cardoso, J. -F.
- IEEE Transactions on Signal Processing, Vol. 45, Issue 2
Combined Use of Groundwater Dating, Chemical, and Isotopic Analyses to Resolve the History and Fate of Nitrate Contamination in Two Agricultural Watersheds, Atlantic Coastal Plain, Maryland
journal, September 1995
- Böhlke, J. K.; Denver, J. M.
- Water Resources Research, Vol. 31, Issue 9
Identification of a time-dependent source term in nonlinear hyperbolic or parabolic heat equation
journal, December 2015
- Borukhov, V. T.; Zayats, G. M.
- International Journal of Heat and Mass Transfer, Vol. 91
Algorithm quasi‐optimal (AQ) learning
journal, March 2010
- Cervone, Guido; Franzese, Pasquale; Keesee, Allen P. K.
- Wiley Interdisciplinary Reviews: Computational Statistics, Vol. 2, Issue 2
Artificial intelligence for management and control of pollution minimization and mitigation processes
journal, March 2003
- Chan, Christine W.; Huang, Guo H.
- Engineering Applications of Artificial Intelligence, Vol. 16, Issue 2
Multivariate statistical and GIS-based approach to identify heavy metal sources in soils
journal, October 2001
- Facchinelli, A.; Sacchi, E.; Mallen, L.
- Environmental Pollution, Vol. 114, Issue 3
The Representation and Matching of Pictorial Structures
journal, January 1973
- Fischler, M. A.; Elschlager, R. A.
- IEEE Transactions on Computers, Vol. C-22, Issue 1
Identification of Contaminant Sources in Water Distribution Systems Using Simulation–Optimization Method: Case Study
journal, July 2006
- Guan, Jiabao; Aral, Mustafa M.; Maslia, Morris L.
- Journal of Water Resources Planning and Management, Vol. 132, Issue 4
Inverse source problem in a one-dimensional evolution linear transport equation with spatially varying coefficients: application to surface water pollution
journal, September 2013
- Hamdi, Adel; Mahfoudhi, Imed
- Inverse Problems in Science and Engineering, Vol. 21, Issue 6
Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga River, Spain) by principal component analysis
journal, February 2000
- Helena, B.
- Water Research, Vol. 34, Issue 3
Applicability of statistical learning algorithms in groundwater quality modeling: GROUNDWATER MODELING BY LEARNING MACHINES
journal, May 2005
- Khalil, Abedalrazq; Almasri, Mohammad N.; McKee, Mac
- Water Resources Research, Vol. 41, Issue 5
Emerging organic contaminants in groundwater: A review of sources, fate and occurrence
journal, April 2012
- Lapworth, D. J.; Baran, N.; Stuart, M. E.
- Environmental Pollution, Vol. 163
Learning the parts of objects by non-negative matrix factorization
journal, October 1999
- Lee, Daniel D.; Seung, H. Sebastian
- Nature, Vol. 401, Issue 6755
Point source identification in nonlinear advection–diffusion–reaction systems
journal, March 2013
- Mamonov, A. V.; Tsai, Y-H R.
- Inverse Problems, Vol. 29, Issue 3
Estimation of historical groundwater contaminant distribution using the adjoint state method applied to geostatistical inverse modeling: ESTIMATION OF GROUNDWATER CONTAMINANT DISTRIBUTION
journal, August 2004
- Michalak, Anna M.; Kitanidis, Peter K.
- Water Resources Research, Vol. 40, Issue 8
Comparison of inverse methods for reconstructing the release history of a groundwater contamination source
journal, September 2000
- Neupauer, Roseanna M.; Borchers, Brian; Wilson, John L.
- Water Resources Research, Vol. 36, Issue 9
Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values
journal, June 1994
- Paatero, Pentti; Tapper, Unto
- Environmetrics, Vol. 5, Issue 2
Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
journal, November 1987
- Rousseeuw, Peter J.
- Journal of Computational and Applied Mathematics, Vol. 20
Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan
journal, April 2007
- Shrestha, S.; Kazama, F.
- Environmental Modelling & Software, Vol. 22, Issue 4
Statistical source identification of metals in groundwater exposed to industrial contamination
journal, May 2007
- Tariq, Saadia R.; Shah, Munir H.; Shaheen, N.
- Environmental Monitoring and Assessment, Vol. 138, Issue 1-3
A Critical Review of the Risks to Water Resources from Unconventional Shale Gas Development and Hydraulic Fracturing in the United States
journal, March 2014
- Vengosh, Avner; Jackson, Robert B.; Warner, Nathaniel
- Environmental Science & Technology, Vol. 48, Issue 15
Line Search Filter Methods for Nonlinear Programming: Motivation and Global Convergence
journal, January 2005
- Wächter, Andreas; Biegler, Lorenz T.
- SIAM Journal on Optimization, Vol. 16, Issue 1
On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming
journal, April 2005
- Wächter, Andreas; Biegler, Lorenz T.
- Mathematical Programming, Vol. 106, Issue 1
Simultaneous parameter estimation and contaminant source characterization for coupled groundwater flow and contaminant transport modelling
journal, July 1992
- Wagner, Brian J.
- Journal of Hydrology, Vol. 135, Issue 1-4
Works referencing / citing this record:
Unsupervised phase mapping of X-ray diffraction data by nonnegative matrix factorization integrated with custom clustering
journal, August 2018
- Stanev, Valentin; Vesselinov, Velimir V.; Kusne, A. Gilad
- npj Computational Materials, Vol. 4, Issue 1
A Comparison of Machine-Learning Methods to Select Socioeconomic Indicators in Cultural Landscapes
journal, November 2018
- Maldonado, Ana; Ramos-López, Darío; Aguilera , Pedro
- Sustainability, Vol. 10, Issue 11
Distributed non-negative matrix factorization with determination of the number of latent features
journal, February 2020
- Chennupati, Gopinath; Vangara, Raviteja; Skau, Erik
- The Journal of Supercomputing
Unsupervised machine learning based on non-negative tensor factorization for analyzing reactive-mixing
journal, October 2019
- Vesselinov, V. V.; Mudunuru, M. K.; Karra, S.
- Journal of Computational Physics, Vol. 395
Targeted Source Detection for Environmental Data
preprint, January 2019
- Zheng, Guanjie; Liu, Mengqi; Wen, Tao
- arXiv