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Journal ArticleDOI

Inverse source problem in a one-dimensional evolution linear transport equation with spatially varying coefficients: application to surface water pollution

TLDR
In this article, the identification of a time-dependent point source occurring in the right-hand side of a one-dimensional evolution linear advection-dispersion-reaction equation is investigated.
Abstract
This paper deals with the identification of a time-dependent point source occurring in the right-hand side of a one-dimensional evolution linear advection–dispersion–reaction equation. The originality of this study consists in considering the general case of transport equations with spatially varying dispersion, velocity and reaction coefficients which enables to extend the applicability of the obtained results to various areas of science and engineering. We derive a main condition on the involved spatially varying coefficients that yields identifiability of the sought source, provided its time-dependent intensity function vanishes before reaching the final monitoring time, from recording the generated state at two observation points framing the source region. Then, we establish an identification method that uses those records to determine the elements defining the sought source. Some numerical experiments on a variant of the surface water pollution model are presented.

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Citations
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Journal ArticleDOI

Contaminant source identification using semi-supervised machine learning.

TL;DR: 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 is proposed.
Journal ArticleDOI

Source identification of sudden contamination based on the parameter uncertainty analysis

TL;DR: A source identification framework which considers the uncertainty of the model9s sensitive parameters and combines Bayesian inference and Markov Chain Monte Carlo algorithms simulation is established, and the South-to-North Water Diversion Project is taken as the case study.
Journal ArticleDOI

Unsupervised machine learning based on non-negative tensor factorization for analyzing reactive-mixing

TL;DR: In this article, a non-negative tensor factorization (NTF) method is applied to a large set of high-resolution finite-element model simulations representing anisotropic reaction-diffusion processes in perturbed vortex-based velocity fields.
Journal ArticleDOI

Multi-point source identification of sudden water pollution accidents in surface waters based on differential evolution and Metropolis–Hastings–Markov Chain Monte Carlo

TL;DR: In this paper, a new method is designed by combining differential evolution algorithm (DEA) and Metropolis-Hastings-Markov Chain Monte Carlo (MH-MCMC) based on Bayesian inference to identify multi-point sudden water pollution sources.
References
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Book

Standard methods for the examination of water and wastewater

TL;DR: The most widely read reference in the water industry, Water Industry Reference as discussed by the authors, is a comprehensive reference tool for water analysis methods that covers all aspects of USEPA-approved water analysis.
Book

Théorie des distributions

TL;DR: The merite as discussed by the authors is a date marque une date dans le progres des mathematiques and de la physique en levant l'ambiguite que constituait le succes des methodes de calcul symbolique aupres des physiciens and l'inacceptabilite de leurs formules au regard de la rigueur mathematiques.
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