Linking a simulated annealing based optimization model with PHT3D simulation model for chemically reactuve transport processes to optimally characterize unknown contaminant sources in a former mine site in Australia

Historical mining activities often lead to continuing wide spread contaminants in both groundwater and surface water in previously operational mine site areas. The contamination may continue for many years after closing down the mining activities. The essential first step for sustainable management of groundwater and development of remediation strategies is the unknown contaminant source characterization. In a mining site, there are multiple species of contaminants involving complex geochemical processes. It is difficult to identify the potential sources and pathways incorporating the chemically reactive multiple species of contaminants making the source characterization process more challenging. To address this issue, a reactive transport simulation model PHT3D is linked to a Simulated Annealing based the optimum decision model. The numerical simulation model PHT3D is utilized for numerically simulating the reactive transport process involving multiple species in the former mine site area. The simulation results from the calibrated PHT3D model are illustrated, with and without incorporating the chemical reactions. These comparisons show the utility of using a reactive, geochemical transport process’ simulation model. Performance evaluation of the linked simulation optimization methodology is evaluated for a contamination scenario in a former mine site in Queensland, Australia. These performance evaluation results illustrate the applicability of linked simulation optimization model to identify the source characteristics while using PHT3D as a numerical reactive chemical species’ transport simulation model for the hydro-geochemically complex aquifer study area

Preliminary hydrogeological modeling and optimal monitoring network design for a contaminated abandoned mine site area: application of developed monitoring network design software

In abandoned mine sites, i.e., mine sites where mining operations have ended, wide spread contaminations are often evident, but the potential sources and pathways of contamination especially through the subsurface, are difficult to identify due to inadequate and sparse geochemical measurements available. Therefore, it is essential to design and implement a planned monitoring net-work to obtain essential information required for establishing the potential contamination source locations, i.e., waste dumps, tailing dams, pits and possible pathways through the subsurface, and to design a remediation strategy for rehabilitation. This study presents an illustrative application of modeling the flow and transport processes and monitoring network design in a study area hydrogeologically resembling an abandoned mine site in Queensland, Australia. In this preliminary study, the contaminant transport process modeled does not incorporate the reactive geochemistry of the contaminants. The transport process is modeled considering a generic conservative contaminant for the illustrative purpose of showing the potential application of an optimal monitoring design methodology. This study aims to design optimal monitoring network to: 1) minimize the contaminant solute mass estimation error; 2) locate the plume boundary; 3) select the monitoring locations with (potentially) high concentrations. A linked simulation optimization based methodology is utilized for optimal monitoring network design. The methodology is applied utilizing a recently developed software package CARE-GWMND, developed at James Cook University for optimal monitoring network design. Given the complexity of the groundwater systems and the sparsity of pollutant concentration observation data from the field, this software is capable of simulating the groundwater flow and solute transport with spatial interpolation of data from a sparse set of available data, and it utilizes the optimization algorithm to determine optimum locations for implementing monitoring wells

Predicting the valence of a scene from observers’ eye movements

Multimedia analysis benefits from understanding the emotional content of a scene in a variety of tasks such as video genre classification and content-based image retrieval. Recently, there has been an increasing interest in applying human bio-signals, particularly eye movements, to recognize the emotional gist of a scene such as its valence. In order to determine the emotional category of images using eye movements, the existing methods often learn a classifier using several features that are extracted from eye movements. Although it has been shown that eye movement is potentially useful for recognition of scene valence, the contribution of each feature is not well-studied. To address the issue, we study the contribution of features extracted from eye movements in the classification of images into pleasant, neutral, and unpleasant categories. We assess ten features and their fusion. The features are histogram of saccade orientation, histogram of saccade slope, histogram of saccade length, histogram of saccade duration, histogram of saccade velocity, histogram of fixation duration, fixation histogram, top-ten salient coordinates, and saliency map. We utilize machine learning approach to analyze the performance of features by learning a support vector machine and exploiting various feature fusion schemes. The experiments reveal that ‘saliency map’, ‘fixation histogram’, ‘histogram of fixation duration’, and ‘histogram of saccade slope’ are the most contributing features. The selected features signify the influence of fixation information and angular behavior of eye movements in the recognition of the valence of images

Development of integrated methodologies for optimal characterization of reactive contaminant sources and monitoring network design in polluted aquifer sites

Groundwater is one of the most important natural resources in many parts of the world; however it is widely polluted due to human activities. Reliable groundwater management and remediation strategies are generally developed following the identification of groundwater pollution sources, where the measured data from monitoring locations are utilized to estimate the unknown pollutant source location, magnitude and duration of activity. However, accurately identifying characteristics of unknown contaminant sources is a challenging task due to uncertainties in terms of predicting source flux injection, hydro-geological and geo-chemical parameters, and the concentration observation field measurement. Although sufficient concentration measurement data are essential to accurately identify source characteristics, available data are often sparse and limited in quantity. Therefore, this inverse problem of characterizing unknown groundwater pollution sources is often considered ill-posed, complex and non-unique. Different methods have been utilized to identify pollution source characteristics; however, the linked simulation-optimization approach is one effective method to obtain acceptable results under uncertainties in complex real life scenarios. With this approach, the numerical flow and contaminant transport simulation models are externally linked to an optimization algorithm, with the objective of minimizing the difference between measured concentration and estimated pollutant concentration at observation locations. Concentration measurement data are very important to accurately estimate pollution source properties; therefore, optimal design of the monitoring network is essential to gather adequate measured data at desired times and locations. A simulation model should be utilized to accurately describe the aquifer processes properties in terms of hydro-geochemical parameters and boundary conditions. However, the simulation of the transport processes becomes complex when the pollutants are chemically reactive. An additional difficulty with linked simulation-optimization models is that an optimal solution generally requires huge computation time, due to iterative repeated solution of the numerical flow and transport simulation models. To address this, Genetic Programming based surrogate models may be used to approximate the numerical simulation model in the linked simulation-optimization model for source characterization. Therefore, the aim of the present study is to demonstrate the feasibility and efficiency of a developed methodology to optimally identify or characterize the unknown distributed pollution sources with chemically reactive species in complex contaminated aquifers. Because the accuracy and reliability of the source characterization process depends on the quality and extent of the spatial and temporal concentration measurement data, a relevant issue is the design and implementation of a suitable and efficient monitoring network under conditions of various uncertainties. This is especially true, where the initial measurement data available are sparse and obtained from arbitrary monitoring locations. Therefore a new two objective Pareto optimal monitoring network design methodology is developed. This design methodology utilizes Fractal Singularity Mapping Technique to determine plume boundaries, information used to select potential monitoring locations for contaminant concentration monitoring. This approach substantially improves the source characterization efficiency as demonstrated for illustrative study areas. In order to improve the efficiency and accuracy of the source characterization methodology in real life sites where contamination is evident, but the monitoring data are very sparse and arbitrary, the monitoring network design model is integrated with the source characterization process by sequentially utilizing the source characterization model to estimate the sources. This information then, is utilized to design and implement a cost effective monitoring network. This sequential and iterative methodology is shown to improve the source characterization efficiency and accuracy, even when dealing with a hydrogeochemically complex aquifer system with multiple reactive species. The performance of the linked source characterization model is also evaluated by limited application to real life sites, which included a complex, abandoned mine site in Queensland, Australia. The sequential source characterization and monitoring network design methodology is applied to a contaminated aquifer in an urban area in Australia. Several techniques are utilized in the proposed methodology to increase the efficiency of the source characterization including trained and tested Genetic Programming based surrogate models, Adaptive Simulated Annealing optimization algorithm, Fractal Singularity Mapping Technique, and Statistical Kriging interpolation. The study includes the following steps: 1. The flow and reactive contaminant transport simulation model is utilized to simulate the aquifer processes; 2. Trained Genetic Programming (GP) based meta-models are developed using the simulated response of the aquifer to randomly generated source fluxes. The selected GP models replace the numerical simulation model in the linked simulation-optimization model for source characterization. 3. Two objectives Pareto-optimal design of a monitoring network for sequential characterization of pollutant sources uses a linked simulation-optimization model incorporating Adaptive Simulated Annealing as the optimization algorithm. 4. Integrated source identification and monitoring network design is carried out to obtain sufficient accuracy in characterization of source properties. 5. The performance of the developed methodologies is evaluated by limited application of the developed methodologies to real life sites

Fractal singularity-based multiobjective monitoring networks for reactive species contaminant source characterization

The first step in effective design of contaminated aquifer site remediation is the accurate characterization of contaminant sources, which requires a large amount of concentration measurement data. However, in real-world scenarios contamination monitoring wells are generally arbitrary in location, monitoring data are sparse in time and space, and there are various uncertainties in predicting the transport process. It is a very challenging problem to optimally design effective monitoring networks intended for accurate unknown contaminant source characterization, with multiple potential source locations. In this study, the local singularity mapping technique is utilized to obtain potential monitoring well locations, which are used as input to the optimal network design model. This set of potential monitoring locations is utilized for selecting the subset of the optimal monitoring locations. This method of selecting the set of potential locations can improve the efficiency of the designed monitoring network for source characterization. The proposed methodology utilizes a multiobjective optimization algorithm for solving a two-objective optimal monitoring network design model. The optimization model is linked to a numerical simulation model simulating flow and transport processes in the aquifer. While constraining the maximum number of permissible monitoring locations, the designed optimal monitoring network improves the accuracy of unknown contaminant source characterization. The designed monitoring network can decrease the degree of nonuniqueness in the measured set of possible aquifer responses to geochemical stresses. The potential application of the developed methodology is demonstrated by evaluating the performance for an illustrative contaminated mine site aquifer. These performance evaluation results show the improved efficiency in source characterization when concentration measurements from the designed monitoring network are utilized

Approximate simulation of complex geochemical transport processes by genetic programming based surrogate models with application to a closed down mine site in Queensland Australia

Transport of reactive chemical contaminant species in groundwater aquifers is a complex and highly non-linear physical and geochemical process especially for real life scenarios. Simulating this transport process involves solving complex nonlinear equations and generally requires huge computational time for a given aquifer study area. Development of optimal remediation strategies in aquifers may require repeated solution of such complex numerical simulation models. To overcome this computational limitation and improve the computational feasibility of large number of repeated simulations, Genetic Programming based trained surrogate models are developed to approximately simulate such complex transport processes. Transport process of acid mine drainage, a hazardous pollutant is first simulated using a numerical simulated model: HYDROGEOCHEM 5.0 for a contaminated aquifer in a historic mine site. Simulation model solution results for an illustrative contaminated aquifer site is then approximated by training and testing a Genetic Programming (GP) based surrogate model. Performance evaluation of the ensemble GP models as surrogate models for the reactive species transport in groundwater demonstrates the feasibility of its use and the associated computational advantages. The results show the efficiency and feasibility of using ensemble GP surrogate models as approximate simulators of complex hydrogeologic and geochemical processes in a contaminated groundwater aquifer incorporating uncertainties in historic mine site

Linked optimal reactive contaminant source characterization in contaminated mine sites: case study

Application of a developed methodology for characterization of distributed contaminant sources in a contaminated mine site aquifer involving complex geochemical processes is presented. Linked simulation-optimization models are widely used as efficient tools for identifying unknown groundwater pollution sources. This is an essential first step in determining effective and reliable groundwater management and remediation strategies for a polluted aquifer. However, linking robust numerical models to simulate the transport processes of reactive chemical contaminant species in aquifers involving complex and highly nonlinear physical and geochemical process increases the computational burden extensively, and may affect the feasibility and efficiency of the methodology. To overcome this computational limitation and improve the computational feasibility by avoiding the necessity of repeated numerical simulations, genetic programming-based trained surrogate models are developed to approximately simulate such complex transport processes. Simulation model solution results are then approximated by training and testing genetic programming (GP)-based surrogate models. Performance evaluation of the GP models as surrogate models for the reactive species transport in groundwater demonstrates the feasibility of its use and the associated computational advantages. Some of the hydrogeologic parameter uncertainties are also incorporated in the surrogate models by using training samples obtained by random perturbation of the parameters. The linked simulation-optimization based methodology is evaluated for a contaminated former mining site in Queensland, Australia, as a limited case study

Approximate simulation of complex geochemical transport processes by genetic programming based surrogate models with application to a closed down mine site in Queensland Australia

Transport of reactive chemical contaminant species in groundwater aquifers is a complex and highly non-linear physical and geochemical process especially for real life scenarios. Simulating this transport process involves solving complex nonlinear equations and generally requires huge computational time for a given aquifer study area. Development of optimal remediation strategies in aquifers may require repeated solution of such complex numerical simulation models. To overcome this computational limitation and improve the computational feasibility of large number of repeated simulations, Genetic Programming based trained surrogate models are developed to approximately simulate such complex transport processes. Transport process of acid mine drainage, a hazardous pollutant is first simulated using a numerical simulated model: HYDROGEOCHEM 5.0 for a contaminated aquifer in a historic mine site. Simulation model solution results for an illustrative contaminated aquifer site is then approximated by training and testing a Genetic Programming (GP) based surrogate model. Performance evaluation of the ensemble GP models as surrogate models for the reactive species transport in groundwater demonstrates the feasibility of its use and the associated computational advantages. The results show the efficiency and feasibility of using ensemble GP surrogate models as approximate simulators of complex hydrogeologic and geochemical processes in a contaminated groundwater aquifer incorporating uncertainties in historic mine site

Recent developments in identification of unknown contamination sources and monitoring network design for contaminated groundwater systems

In order to design an effective aquifer contamination remediation strategy two important steps are: (i) identification of unknown groundwater pollution sources once contamination is detected in an aquifer, and (ii) efficient and effective monitoring of contaminant plume movement. James Cook University, Australia and CRC-CARE are collaborating in developing comprehensive and easy to use computer software and state of art methodologies that at can be utilized for (i) identification of unknown pollution source magnitudes, location, and time of activity; (2) optimal design of a contamination monitoring network that can be implemented in any contaminated groundwater site incorporating site specific information. One part of the collaborative research between James Cook University team and CRC-CARE has resulted in the development of software that enables water resources managers and engineers to solve the difficult problems of identifying sources of pollution in a contaminated groundwater systems, and to design optimal monitoring networks that can detect the extent and movement of contaminants in contaminated groundwater systems. The developed computer software make it possible for practitioners with limited knowledge of hydrogeology and pollutant transport processes to address the source identification issue. These software are expected to be immensely useful for proper management of contaminated sites with unknown sources of contamination. The capabilities of the two developed software, the contamination source identification and the monitoring network design, are briefly introduced