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High-cost remediation of groundwater pollution makes it important to obtain exact information about the source. This is quite difficult to achieve in naturally ill-posed inverse problems of this kind. If the aquifer parameters are also unknown, the problem becomes even more challenging. To address this difficulty, we propose the alternating direction genetic algorithm (ADGA) approach, together with modification of the order of magnitude of the decision variables, to increase the accuracy of the results and computational efficiency. Seven scenarios were designed to test the accuracy of the proposed approach in aquifer with different properties, number of pollution sources, parameters and measurement errors. The results show that combining the ADGA approach with modification of the order of magnitude of the decision variables for identifying both groundwater pollution source and aquifer parameters significantly increases the accuracy of estimated results. The NE value for the estimated results decreased from 9.81% to 58.44% for different cases, and computation time is about half decreased. In addition, the approach is applicable in situations where concentrations of observational data with measurement error, and for multiple source locations and non-uniform media. [ABSTRACT FROM AUTHOR]

The problem of identifying an unknown pollution source in polluted aquifers, based on known contaminant concentrations measurement in the studied areas, is part of the broader group of issues, called inverse problems. This paper investigates the feasibility of using Artificial Neural Networks (ANNs) for solving the inverse problem of locating in time and space the source of a contamination event in a homogeneous and isotropic two dimensional domain. ANNs are trained in order to implement an input-output relationship which associates the position. Once the output of the system is known, the input is reconstructed by inverting the trained ANNs. The approach is applied for studying a theoretical test case where the inverse problem is solved on the basis of measurements of contaminant concentrations in monitoring wells located in the studied area. Groundwater pollution sources are characterized by varying spatial location and duration of activity. To identify these unknown pollution sources, concentration measurements data of monitoring wells are used. If concentration observations are missing over a length of time after an unknown source has become active, it is more difficult to correctly identify the unknown pollution source. In this work, a missing data scenario has been taken into consideration. In particular, a case where only one measurement has been made after the pollutant source interrupted its activity has been considered.

Often untreated wastes are buried underground as convenient method of disposal. These clandestine disposal methods are adopted to offset the cost incurred in proper waste management. Though there might be short term economic gain to the disposer but the long-term impact of such disposal manifests in form of groundwater pollution, which if left unchecked would potentially pollute the entire aquifer. Any contaminated aquifer treatment technique requires prior information about the pollutant sources characteristics. Thus, characterization of unknown groundwater pollution sources becomes imperative in remediation process. The first step involves the identification of number of sources and their locations. In a clandestine scenario the entire study area is treated as a potential source and the search for the actual source can be extensively rigorous in space and time. To overcome this challenge a kriging based zoning is adopted to narrow down the search space for the actual sources in a limited data availability scenario. A comparative analysis of two methods is presented; first method where only (Simulated Annealing based Linked Simulation Optimization) SALSO is used, and second method where kriging based zoning plus SALSO is adopted for pollution source characterization. It is found that kriging method clubbed with SALSO gives significantly better results compared to initial results. A boundary of Indian Institute of Technology Patna with hypothetical hydro-geological parameters is used to test the developed method. The location of the pollutant sources is easily identified when a kriging based Simulated Annealing Linked Simulation Optimization (KSALSO) method is adopted. [ABSTRACT FROM AUTHOR]

Abstract The optimization model is presently used for the identification of pollution sources and it is based on non-linear programming optimization. The decision variables in this model are continuous, resulting in a weak recognition of integer variables including pollution source location. In addition, as the number of pollution sources increase, so the calculated load increases exponentially and accuracy decreases. Compared with previous studies, this study makes a series of improvements by adopting a 0–1 mixed integer nonlinear programming optimization model to enable the simultaneous identification of both location (integer variable) and the release intensity (continuous variable) of the pollution source. One of the constraints in the optimization model is a simulation component which requires thousands of calls during the calculation process and therefore requires considerable computational load. To avoid this problem, the Kriging surrogate model is established in this study to reduce computational load, while at the same time ensuring the accuracy of the simulation results. The identification result is solved using a genetic algorithm (GA) and represents the real location of the pollution source, while release intensities are close to actual ones with small relative errors. The Kriging surrogate model is based on a 0–1 mixed integer nonlinear programming optimization model and can simultaneously identify both the location and the release intensity of the pollution source with a high degree of accuracy and by using short computational times. Highlights • A highly accurate surrogate model of simulation model based on Kriging is established to reduce computing load and time. • Apply 0-1 optimization model based on surrogate model to identify the pollution source location and release intensity. [ABSTRACT FROM AUTHOR]

Using limited observation data of groundwater quality, models of groundwater pollution source identification can be used to estimate the locations, leakage rate, and the dominant processes of the pollution sources, which thus can provide a reference for formulating remedial schemes for groundwater pollution. Based on the principles and theories of pollutant movement and source identification, this paper presents an overview of existing mathematical methods, including direct methods (inverse particle tracing methods and regularization-based methods) and indirect methods (optimization-based and probability-based methods). The main problems in the application of these methods are (1) the complexity of groundwater pollution source identification, (2) groundwater organic contamination, and (3) the low efficiency of model calculation. Integrated research of soil-groundwater systems, instrumentation-based groundwater pollution monitoring, identification of NAPL pollutants, and GPU-based heterogeneous parallel computing will be the keys to groundwater pollution source identification in the future. [ABSTRACT FROM AUTHOR]

Groundwater pollution source identification (GPSI), which is critical for taking effective measures to protect groundwater resources, assess risks, and design remediation strategies, typically involves the solution of a nonlinear and ill-posed inverse problem. Regarding the inversion of dense non-aqueous phase liquid (DNAPL) sources, the special characteristics of pollutants render related research more complex. In the present study, homotopy-based optimization inverse theory and multi-kernel extreme learning machine (MK-ELM) were combined for efficiently solving GPSI problem while estimating aquifer parameters at a DNAPL-polluted site. The extreme learning machine incorporating multi kernels and whose parameters are obtained by means of a genetic algorithm (GA) was embedded in an optimization model for GPSI to replace the multiphase flow simulation model and to mitigate the considerable computational burdens of inversion iteration. The hybrid homotopy-particle swarm optimization (PSO) algorithm was constructed as a more efficient method for segmentally searching the global optimum in wide areas with low dependence on initial values. Results showed that the application of GA-based MK-ELM and hybrid homotopy-PSO effectively accomplish the simultaneous identification of source characteristics and aquifer parameters. The MK-ELM approximate the outputs of multiphase flow simulation model sufficiently with the certainty coefficient (R 2) increased to 0.9982, whereas the mean relative error was limited to 1.5168%. Compared to the widely used PSO algorithm, the hybrid homotopy-PSO algorithm significantly reduced the mean relative error of identification results from 6.77% to 2.89%. • A new approximation model and an improved global optimization method were proposed for efficiently solving GPSI problems. • A multi-kernel extreme learning machine is proposed to approximate the simulation model with higher robustness. • A hybrid homotopy-PSO algorithm is constructed as a more efficient tool for searching the global optimum in wide areas. [ABSTRACT FROM AUTHOR]