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Ann based approach to solve groundwater pollution inverse problem
No full textThis paper investigates the feasibility of solving the groundwater pollution inverse problem by using Artificial Neural Networks (ANNs). Different ANNs have been trained to solve the direct problem with the objective of associating the input patterns with the output patterns. In order to solve the inverse problem and to identify the unknown pollution source and their characteristics, the trained ANN is inverted. By fixing the output pattern of the ANN, the proposed procedure is able to reconstruct the corresponding input. The approach has been applied for a real case which deals with the contamination of the Rhine aquifer by carbon tetrachloride (CCl4) due to a tanker accident. This case is well adapted to the problem since numerous concentrations have been measured at different piezometers and at different time. The location of the source and the beginning of the contamination are known. The ANNs are used to identify the contamination source and the results are compared with the solution obtained with a different approach
ANN-based approach for the estimation aquifer pollutant source behaviour
No full textThe problem of identifying an unknown pollution source in polluted aquifers, based on known contaminant concentrations measurement, is part of the broader group of issues, called inverse problems. This paper investigates the feasibility of solving the groundwater pollution inverse problem by using artificial neural networks (ANNs). The approach consists first in training an ANN to solve the direct problem, where the pollutant concentration in a set of monitoring wells is calculated for a known pollutant source. Successively, the trained ANN is frozen and it is used to solve the inverse problem, where the pollutant source is calculated which corresponds to a set of concentrations in the monitoring wells. The approach has been applied for a real case which deals with the contamination of the Rhine aquifer by carbon tetrachloride (CCl4) due to a tanker accident. The obtained results are compared with the solution obtained with a different approach retrieved from literature. The results show the suitability of ANNs-based methods for solving inverse non-linear problems
Polluted aquifer inverse problem solution using artificial neural networks
No full textThe 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
Artificial neural networks and weight of evidence method to landslide susceptibility assessment
No full textIdentification of the unknown pollution source in the Alsatian aquifer (France) through groundwater modelling and Artificial Neural Networks applications
No full textPrediction of nitrate concentration in groundwater using an Artificial Neural Network (ANN) approach
No full textThis paper evaluates the effectiveness of Artificial Neural Networks (ANNs) for the estimation of the nitrate concentration in a study area located in the Nitrate Vulnerable Zone (NVZ) of the Arborea plain (Sardinia - Italy). Agricultural derived nitrate contamination of groundwater has been estimated by using easily and economical quantifiable parameters such as pH, electrical conductivity, temperature, groundwater level. Data used for training and validating the ANNs derive from a set of 225 measurements coming from 12 piezometers distributed in the study area. In order to define the best topology of the ANN and the best dimension of respectively the training and the validation sets a growing procedure has been applied
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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