Solving Inverse Problems of Unknown Contaminant Source in Groundwater-River Integrated Systems Using a Surrogate Transport Model Based Optimization

Abstract

The paper presents a new approach to identify the unknown characteristics (release history and location) of contaminant sources in groundwater, starting from a few concentration observations at monitoring points. An inverse method that combines the forward model and an optimization algorithm is presented. To speed up the computation, the transfer function theory is applied to create a surrogate transport forward model. The performance of the developed approach is evaluated on two case studies (literature and a new one) under different scenarios and measurement error conditions. The literature case study regards a heterogeneous confined aquifer, while the proposed case study was never investigated before, it involves an aquifer-river integrated flow and transport system. In this case, the groundwater contaminant originated from a damaged tank, migrates to a river through the aquifer. The approach, starting from few concentration observations monitored at a downstream river cross-section, accurately estimates the release history at a groundwater contaminant source, even in presence of noise on observations. Moreover, the results show that the methodology is very fast, and can solve the inverse problem in much less computation time in comparison with other existing approaches.

Identification of aquifer pollution’s point sources with the reciprocity principle

“…We have tested the effect of different levels of noise on the identification process, by adding an uniform white noise, with zero mean, to the pollution concentration measurement . The noise levels from 2 to 8% that we tested, are of the same orders of magnitude as those found in similar studies (between 5 and 10% 33 ).…”

Contaminant source identification in groundwater by means of artificial neural network

“…The simulation model solves the flow and transport equations for given initial and boundary conditions. Then, the differences between simulated and observed data are minimized through an optimization algorithm (Ayvaz, 2010;Jamshidi et al 2020). The reader is referred to Gómez-Hernández and and to Barati Moghaddam et al (2021) for extensive reviews of the source reconstruction problem in groundwater hydrology and groundwater-surface hydrology, respectively.…”

Determining the Source Location of Groundwater Contamination by Weierstrass’ Polynomial Approximation

“…Amiri et al (2019) proposed a feasible framework to nd the source release history in a river by solving 1-D, 2-D and 3-D inverse solute transport equation. Jamshidi et al (2020) developed a technique to identify the source location and contaminant release history in river considering contaminant concentration at observed points as input data. In this paper we used the Weierstrass polynomial approximation method which has several advantages.…”