Journal ArticleDOI
Breakthrough Curves Characterization and Identification of an Unknown Pollution Source in Groundwater System Using an Artificial Neural Network (ANN)
Divya Srivastava,Raj Mohan Singh +1 more
TLDR
In this paper, a feed-forward multilayer artificial neural network (ANN) was used to identify the sources in terms of its location, magnitudes, and duration of activity.About:
This article is published in Environmental Forensics.The article was published on 2014-03-28. It has received 29 citations till now. The article focuses on the topics: Groundwater flow.read more
Citations
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Journal ArticleDOI
A Kriging surrogate model coupled in simulation-optimization approach for identifying release history of groundwater sources
TL;DR: The results of three hypothetical cases demonstrate that the Kriging model has the ability to solve groundwater contaminant source problems that could occur during field site source identification problems with a high degree of accuracy and short computation times and is thus very robust.
Journal ArticleDOI
New approach for point pollution source identification in rivers based on the backward probability method.
TL;DR: A new approach for point source identification of sudden water pollution in rivers, which aims to determine where (source location), when (release time) and how much pollutant was introduced into the river, is proposed.
Journal ArticleDOI
Groundwater System Modeling for Simultaneous Identification of Pollution Sources and Parameters with Uncertainty Characterization
Divya Srivastava,Raj Mohan Singh +1 more
TL;DR: In this paper, a Groundwater flow and transport simulation model is used to generate necessary data for Artificial Neural Networks (ANN) model building processes, and breakthrough curves obtained for specified pollution scenario are characterized to reduce the inputs to ANN model.
Journal ArticleDOI
Identifying groundwater contaminant sources based on a KELM surrogate model together with four heuristic optimization algorithms
TL;DR: To improve the efficiency of identifying groundwater contaminant sources, a kernel-based extreme learning machine was used as a surrogate for the time-consuming simulation model, and four heuristic search algorithms were used to improve the accuracy of the identification results.
Journal ArticleDOI
Comparative study of surrogate models for groundwater contamination source identification at DNAPL-contaminated sites
TL;DR: It was found that the KELM model was the most accurate surrogate model, and its performance was significantly improved after parameter optimization, which considerably reduced the computational burden of the simulation–optimization process and also maintained high computation accuracy.
References
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Book
Neural networks for pattern recognition
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Book ChapterDOI
Neural Networks for Pattern Recognition
Suresh Kothari,Heekuck Oh +1 more
TL;DR: The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue.
Book
Dynamics of fluids in porous media
TL;DR: In this paper, the Milieux poreux Reference Record was created on 2004-09-07, modified on 2016-08-08 and the reference record was updated in 2016.
Book
Hydraulics of Groundwater
TL;DR: The reference record was created on 2004-09-07, modified on 2016-08-08 as discussed by the authors, using the reference record of the Ecoulement souterrain reference record.
Journal ArticleDOI
Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications
Holger R. Maier,Graeme C. Dandy +1 more
TL;DR: The steps that should be followed in the development of artificial neural network models are outlined, including the choice of performance criteria, the division and pre-processing of the available data, the determination of appropriate model inputs and network architecture, optimisation of the connection weights (training) and model validation.
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