Evaluation of a distributed numerical simulation optimization approach applied to aquifer remediation

AbstractIn this paper we evaluate a distributed approach which uses numerical simulation and optimization techniques to automatically find remediation solutions to a hypothetical contaminated aquifer. The repeated execution of the numerical simulation model of the aquifer through the optimization cycles tends to be computationally expensive. To overcome this drawback, the numerical simulations are executed in parallel using a network of heterogeneous workstations. Performance metrics for heterogeneous environments are not trivial; a new way of calculating speedup and efficiency for Bag-of-Tasks (BoT) applications is proposed. The performance of the parallel approach is evaluated

Supervised intelligent committee machine method for hydraulic conductivity estimation

Hydraulic conductivity is the essential parameter for groundwater modeling and management. Yet estimation of hydraulic conductivity in a heterogeneous aquifer is expensive and time consuming. In this study; artificial intelligence (AI) models of Sugeno Fuzzy Logic (SFL), Mamdani Fuzzy Logic (MFL), Multilayer Perceptron Neural Network associated with Levenberg-Marquardt (ANN), and Neuro-Fuzzy (NF) were applied to estimate hydraulic conductivity using hydrogeological and geoelectrical survey data obtained from Tasuj Plain Aquifer, Northwest of Iran. The results revealed that SFL and NF produced acceptable performance while ANN and MFL had poor prediciton. A supervised intelligent committee machine (SICM), which combines the results of individual AI models using a supervised artificial neural network, was developed for better prediction of the hydraulic conductivity in Tasuj plain. The performance of SICM was also compared to those of the simple averaging and weighted averaging intelligent committee machine (ICM) methods. The SICM model produced reliable estimates of hydraulic conductivity in heterogeneous aquifers

INFORMATION SYSTEMS FACILITATING GROUNDWATER SUSTAINABILITY MANAGEMENT

Groundwater resources are a major source of drinking water and increasingly require management to be sustained. Achieving this requires IS (Information Systems) supporting the revelation of contamination information from data obtained via monitoring projects. For effective contamination control, its type, size, structure and degree must be unveiled. Unfortunately, when contamination occurs such holistic information is not readily available from monitoring data. Monitoring groundwater quality is limited to specific locations, namely the monitoring wells. Hence, from limited and fragmented data, contamination information must quickly be implied. This study analyzes alternatives of designing IS to facilitate contamination control from the limited sources of data. For this purpose we analyze the monitoring process and different methodologies for data collection from monitoring wells. We have analyzed the efficiency of the various methods with an aquifer domain comprising a small part of the Coastal Plain Aquifer (CPA) of Israel. The results suggest that systematic sampling approaches are the most efficient for attaining sustainability goals

Optimization of Palladium-Catalyzed in Situ Destruction of Trichloroethylene-Contaminated Groundwater Using a Genetic Algorithm

Conventional technologies for the treatment of groundwater contaminated with chlorinated solvents have limitations that have motivated development of innovative technologies. One such technology currently under development involves using palladium-on-alumina (Pd/Al) as a catalyst to promote dechlorination. Pd/Al catalyst may be used in-well as part of a re-circulating horizontal flow treatment well (HFTW) system. An HFTW system involves two or more dual-screened wells, with in-well reactors, to capture and treat contaminated groundwater without the need to pump the water to the surface. In this study, objective and fitness functions, based on system costs and TCE concentration requirements, were developed to optimize a dual-well HFTW system with in-well Pd/Al reactors in a two-aquifer remediation scenario. A genetic algorithm (GA) was coupled with a three dimensional numerical model of contaminant fate and transport to determine optimized HFTW control parameters (well location, pumping rate, and reactor size). The GA obtained a solution within the specified constraints, but the solution was an artificial solution, as contaminated groundwater in one of the two aquifers received no treatment. Based on these results, new objective and fitness functions were developed in an effort to determine the most cost effective solution to remove contaminant mass from the aquifer. The solution arrived at using this approach, while resulting in minimized values of cost per contaminant mass destroyed, produced unacceptably high downgradient contaminant concentration levels. We conclude that by specifying that only two wells could be used in the HFTW system, we overconstrained the problem and that a multi-well HFTW solution is required

Development and evaluation of models for assessing geochemical pollution sources with multiple reactive chemical species for sustainable use of aquifer systems: source characterization and monitoring network design

Michael designed a groundwater flow and reactive transport optimization model. He applied this model to characterize contaminant sources in Australia's first large scale uranium mine site in the Northern Territory. He identified the contamination sources to the groundwater system in the area. His findings will assist planning actions and steps needed to implement the mitigation strategy of this contaminated aquifer

Application Of Adaptive Extended Kalman Filtering Scheme To Improve The Efficiency Of A Groundwater Contaminant Transport Model

Pollution of groundwater can be harmful to the environment. The use of subsurface contaminant transport models, combined with stochastic data assimilation schemes, can give on-target predictions of contaminant transport to enhance the reliability of risk assessment in the area of environmental remediation. Observation data are required to guide the deterministic system model to assimilate the true state of the contaminant. Modeling the behavior of contaminant in groundwater is imperative in predicting the fate of the pollutant, in risk assessment, and as a preceding step of the remission process. In this study a two-dimensional transport model with advection and dispersion is used as the deterministic model of contaminant transport in the subsurface. An Adaptive Extended Kalman filter (AEKF) is constructed as a stochastic data assimilation scheme to meliorate the prediction of the contaminant concentration. Simulation results are shown to compare the performance of the numerical, the Extended Kalman filter and the AEKF. The effectiveness of the AEKF is determined by using a root mean square error (RMSE) of pollutant concentrations in contaminant transport modeling. The results of the models indicate that, at the end of the simulation, the introduction of the Extended Kalman filter improved the deterministic model prediction by reducing the model error from 28 mg/L to 18 mg/L, thus improving the prediction accuracy by approximately 35.7%. The AEKF was successful in reducing the errors in the Extended Kalman filter prediction from 18 mg/L to 11 mg/L hence ameliorating prediction accuracy by approximately 38.9%. In general, the implementation of the AEKF was successful in improving the prediction accuracy of the deterministic model by about 60.7% which shows a substantial improvement in the prediction of the contaminant concentration in the subsurface environment

Application Of Adaptive Extended Kalman Filtering Scheme To Improve The Efficiency Of A Groundwater Contaminant Transport Model

Pollution of groundwater can be harmful to the environment. The use of subsurface contaminant transport models, combined with stochastic data assimilation schemes, can give on-target predictions of contaminant transport to enhance the reliability of risk assessment in the area of environmental remediation. Observation data are required to guide the deterministic system model to assimilate the true state of the contaminant. Modeling the behavior of contaminant in groundwater is imperative in predicting the fate of the pollutant, in risk assessment, and as a preceding step of the remission process. In this study a two-dimensional transport model with advection and dispersion is used as the deterministic model of contaminant transport in the subsurface. An Adaptive Extended Kalman filter (AEKF) is constructed as a stochastic data assimilation scheme to meliorate the prediction of the contaminant concentration. Simulation results are shown to compare the performance of the numerical, the Extended Kalman filter and the AEKF. The effectiveness of the AEKF is determined by using a root mean square error (RMSE) of pollutant concentrations in contaminant transport modeling. The results of the models indicate that, at the end of the simulation, the introduction of the Extended Kalman filter improved the deterministic model prediction by reducing the model error from 28 mg/L to 18 mg/L, thus improving the prediction accuracy by approximately 35.7%. The AEKF was successful in reducing the errors in the Extended Kalman filter prediction from 18 mg/L to 11 mg/L hence ameliorating prediction accuracy by approximately 38.9%. In general, the implementation of the AEKF was successful in improving the prediction accuracy of the deterministic model by about 60.7% which shows a substantial improvement in the prediction of the contaminant concentration in the subsurface environment

GRIDA3—a shared resources manager for environmental data analysis and applications

GRIDA3 (Shared Resources Manager for Environmental Data Analysis and Applications) is a multidisciplinary project designed to deliver an integrated system to forge solutions to some environmental challenges such as the constant increase of polluted sites, the sustainability of natural resources usage and the forecast of extreme meteorological events. The GRIDA3 portal is mainly based on Web 2.0 technologies and EnginFrame framework. The portal, now at an advanced stage of development, provides end-users with intuitive Web-interfaces and tools that simplify job submission to the underneath computing resources. The framework manages the user authentication and authorization, then controls the action and job execution into the grid computing environment, collects the results and transforms them into an useful format on the client side. The GRIDA3 Portal framework will provide a problem-solving platform allowing, through appropriate access policies, the integration and the sharing of skills, resources and tools located at multiple sites across federated domains

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Copyright © 2014 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Environmental Modelling and Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Modelling and Software Vol. 62 (2014), DOI: 10.1016/j.envsoft.2014.09.013The development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g. model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts