Using of neural networks for the prediction of nitrate groundwater contamination in rural and agricultural areas

As a neural network provides a non-linear function mapping of a set of input variables into the corresponding network output, without the requirement of having to specify the actual mathematical form of the relation between the input and output variables, it has the versatility for modeling a wide r...

Full description

Saved in:
Bibliographic Details
Published in:Environmental earth sciences 2012-02, Vol.65 (3), p.917-928
Main Authors: Al-Mahallawi, Khamis, Mania, Jacky, Hani, Azzedine, Shahrour, Isam
Format: Article
Language:English
Subjects:
Aquifers
Biogeosciences
Contamination
Earth and Environmental Science
Earth Sciences
Environmental Science and Engineering
Geochemistry
Geology
Groundwater pollution
Hydrology/Water Resources
Infiltration rate
Land use
Neural networks
Nitrates
Original Article
Terrestrial Pollution
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:As a neural network provides a non-linear function mapping of a set of input variables into the corresponding network output, without the requirement of having to specify the actual mathematical form of the relation between the input and output variables, it has the versatility for modeling a wide range of complex non-linear phenomena. In this study, groundwater contamination by nitrate, the ANNs are applied as a new type of model to estimate the nitrate contamination of the Gaza Strip aquifer. A set of six explanatory variables for 139 sampled wells was used and that have a significant influence were identified by using ANN model. The Multilayer Perceptrons (MLP), Radial Basis Function ( RBF), Generalized Regression Neural Network (GRNN), and Linear Networks were used. The best network found to simulate Nitrate was MLP with six input nodes and four hidden nodes. The input variables are: nitrogen load, housing density in 500-m radius area surrounding wells, well depth, screen length, well discharge, and infiltration rate. The best network found had good performance (regression ratio 0.2158, correlation 0.9773, and error 8.4322). Bivariate statistical test also were used and resulting in considerable unexplained variation in nitrate concentration. Based on ANN model, groundwater contamination by nitrate depends not on any single factor but on the combination of them.
ISSN:1866-6280
1866-6299
DOI:10.1007/s12665-011-1134-5