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Application Of An Artificial Neural Network For Analysis Of Subsurface Contamination At The Schuyler Falls Landfill, Ny, Lance E. Besaw, Donna M. Rizzo, P. J. Mouser
Application Of An Artificial Neural Network For Analysis Of Subsurface Contamination At The Schuyler Falls Landfill, Ny, Lance E. Besaw, Donna M. Rizzo, P. J. Mouser
International Congress on Environmental Modelling and Software
Current subsurface site characterization, plume delineation, remediation designs and monitoring network designs that rely on a limited, albeit large, number of sparsely collected data, tend to be expensive, cumbersome and frequently inadequate for solving multi-objective, long-term environmental management problems. We present a subsurface characterization methodology that integrates multiple types of data using a modified counterpropagation artificial neural network (ANN) to provide parameter estimates and delineate groundwater contamination at a leaking landfill. Apparent conductivity survey data and hydrochemistry data (i.e. heavy metals, BOD5,20, chloride concentration, etc.) are used to estimate the extent of subsurface contamination at the Schuyler Falls Landfill, located …
On The Prediction Of The Ecological Status Of Human-Altered Streams And Its Rule-Based Interpretation, Terence A. Etchells, Alfredo Vellido, E. Martí, Paulo J. G. Lisboa, Joaquim Comas
On The Prediction Of The Ecological Status Of Human-Altered Streams And Its Rule-Based Interpretation, Terence A. Etchells, Alfredo Vellido, E. Martí, Paulo J. G. Lisboa, Joaquim Comas
International Congress on Environmental Modelling and Software
The recent Water Framework Directive of the European Union set year 2015 as their target for freshwater and coastal ecosystems all across Europe to achieve good ecological status. This study concerns the analysis of the empirical data from the knowledge base of an environmental decision support system developed within the European project STREAMES. These data, which come from several low-order streams located mostly on the Mediterranean region, consist of measurements of several physical, chemical and biological variables. We aim to classify these data according to the ecological status of the streams they correspond to, where stream nutrient retention efficiency (a …
Utilizing Artificial Neural Networks To Backtrack Source Location, Zhiqiang Li, Donna M. Rizzo, Nancy Hayden
Utilizing Artificial Neural Networks To Backtrack Source Location, Zhiqiang Li, Donna M. Rizzo, Nancy Hayden
International Congress on Environmental Modelling and Software
Determining the location of the contaminant source is important for improving remediation and site management decisions at many contaminated groundwater sites. At large sites, numerical flow and transport models have been developed that use historical measurement data for calibration. A well-calibrated model is useful for predicting plume migration and other management purposes; however, it is difficult to back out the source with these forward flow and transport models. We present a novel technique utilizing Artificial Neural Networks (ANNs) to backtrack source location and earlier plume concentrations from recent plume information. For proof-of-concept, two tracer tests (a non-point-source and a point-source) …
Application Of An Artificial Neural Network And Stochastic Simulation At The Schuyler Falls Landfill, Ny, Lance E. Besaw, Donna M. Rizzo
Application Of An Artificial Neural Network And Stochastic Simulation At The Schuyler Falls Landfill, Ny, Lance E. Besaw, Donna M. Rizzo
International Congress on Environmental Modelling and Software
Stochastic conditional simulation techniques have been developed to address issues of risk and uncertainty associated with spatially distributed phenomena in earth sciences (e.g. hydraulic conductivity, contaminant concentration, etc.). These techniques (e.g. Gaussian simulation, p-field simulation, turning bands algorithm, etc) are used to assess risk and uncertainty resulting from sparsely sampled field measurements, high measurement variability and incomplete site knowledge. Scientists and engineers are able to model the spatial continuity and quantify uncertainty associated with spatially distributed phenomena through the generation and analysis of many equiprobable stochastic simulations. Uncertainty in contaminated site characterization is of serious concern in environmental engineering. Stochastic …
Application Of An Artificial Neural Network For Analysis Of Subsurface Contamination At The Schuyler Falls Landfill, Ny, Lance E. Besaw, Donna M. Rizzo, P. J. Mouser
Application Of An Artificial Neural Network For Analysis Of Subsurface Contamination At The Schuyler Falls Landfill, Ny, Lance E. Besaw, Donna M. Rizzo, P. J. Mouser
International Congress on Environmental Modelling and Software
Current subsurface site characterization, plume delineation, remediation designs and monitoring network designs that rely on a limited, albeit large, number of sparsely collected data, tend to be expensive, cumbersome and frequently inadequate for solving multi-objective, long-term environmental management problems. We present a subsurface characterization methodology that integrates multiple types of data using a modified counterpropagation artificial neural network (ANN) to provide parameter estimates and delineate groundwater contamination at a leaking landfill. Apparent conductivity survey data and hydrochemistry data (i.e. heavy metals, BOD5,20, chloride concentration, etc.) are used to estimate the extent of subsurface contamination at the Schuyler Falls Landfill, located …
On The Prediction Of The Ecological Status Of Human-Altered Streams And Its Rule-Based Interpretation, Terence A. Etchells, Alfredo Vellido, E. Martí, Paulo J. G. Lisboa, Joaquim Comas
On The Prediction Of The Ecological Status Of Human-Altered Streams And Its Rule-Based Interpretation, Terence A. Etchells, Alfredo Vellido, E. Martí, Paulo J. G. Lisboa, Joaquim Comas
International Congress on Environmental Modelling and Software
The recent Water Framework Directive of the European Union set year 2015 as their target for freshwater and coastal ecosystems all across Europe to achieve good ecological status. This study concerns the analysis of the empirical data from the knowledge base of an environmental decision support system developed within the European project STREAMES. These data, which come from several low-order streams located mostly on the Mediterranean region, consist of measurements of several physical, chemical and biological variables. We aim to classify these data according to the ecological status of the streams they correspond to, where stream nutrient retention efficiency (a …
Utilizing Artificial Neural Networks To Backtrack Source Location, Zhiqiang Li, Donna M. Rizzo, Nancy Hayden
Utilizing Artificial Neural Networks To Backtrack Source Location, Zhiqiang Li, Donna M. Rizzo, Nancy Hayden
International Congress on Environmental Modelling and Software
Determining the location of the contaminant source is important for improving remediation and site management decisions at many contaminated groundwater sites. At large sites, numerical flow and transport models have been developed that use historical measurement data for calibration. A well-calibrated model is useful for predicting plume migration and other management purposes; however, it is difficult to back out the source with these forward flow and transport models. We present a novel technique utilizing Artificial Neural Networks (ANNs) to backtrack source location and earlier plume concentrations from recent plume information. For proof-of-concept, two tracer tests (a non-point-source and a point-source) …
Application Of An Artificial Neural Network And Stochastic Simulation At The Schuyler Falls Landfill, Ny, Lance E. Besaw, Donna M. Rizzo
Application Of An Artificial Neural Network And Stochastic Simulation At The Schuyler Falls Landfill, Ny, Lance E. Besaw, Donna M. Rizzo
International Congress on Environmental Modelling and Software
Stochastic conditional simulation techniques have been developed to address issues of risk and uncertainty associated with spatially distributed phenomena in earth sciences (e.g. hydraulic conductivity, contaminant concentration, etc.). These techniques (e.g. Gaussian simulation, p-field simulation, turning bands algorithm, etc) are used to assess risk and uncertainty resulting from sparsely sampled field measurements, high measurement variability and incomplete site knowledge. Scientists and engineers are able to model the spatial continuity and quantify uncertainty associated with spatially distributed phenomena through the generation and analysis of many equiprobable stochastic simulations. Uncertainty in contaminated site characterization is of serious concern in environmental engineering. Stochastic …