Multi-objective management of saltwater intrusion in coastal aquifers using genetic programming and modular neural network based surrogate models
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Sreekanth2010245,
-
author = "J. Sreekanth and Bithin Datta",
-
title = "Multi-objective management of saltwater intrusion in
coastal aquifers using genetic programming and modular
neural network based surrogate models",
-
journal = "Journal of Hydrology",
-
volume = "393",
-
number = "3-4",
-
pages = "245--256",
-
year = "2010",
-
ISSN = "0022-1694",
-
DOI = "doi:10.1016/j.jhydrol.2010.08.023",
-
URL = "http://www.sciencedirect.com/science/article/B6V6C-50X3N5F-6/2/07d7a64a570c1efe71d9b3a1c71ffb34",
-
keywords = "genetic algorithms, genetic programming, Salinity
intrusion, Coastal aquifer, Pumping optimisation,
Surrogate model, Modular neural network",
-
abstract = "Surrogate model based methodologies are developed for
evolving multi-objective management strategies for
saltwater intrusion in coastal aquifers. Two different
surrogate models based on genetic programming (GP) and
modular neural network (MNN) are developed and linked
to a multi-objective genetic algorithm (MOGA) to derive
the optimal pumping strategies for coastal aquifer
management, considering two objectives. Trained and
tested surrogate models are used to predict the
salinity concentrations at different locations
resulting due to groundwater extraction. A two-stage
training strategy is implemented for training the
surrogate models. Surrogate models are initially
trained with input patterns selected uniformly from the
entire search space and optimal management strategies
based on the model predictions are derived from the
management model. A search space adaptation and model
retraining is performed by identifying a modified
search space near the initial optimal solutions based
on the relative importance of the variables in salinity
prediction. Retraining of the surrogate models is
performed using input-output samples generated in the
modified search space. Performance of the methodologies
using GP and MNN based surrogate models are compared
for an illustrative study area. The capability of GP to
identify the impact of input variables and the
resulting parsimony of the input variables helps in
developing efficient surrogate models. The developed GP
models have lesser uncertainty compared to MNN models
as the number of parameters used in GP is much lesser
than that in MNN models. Also GP based model was found
to be better suited for optimisation using adaptive
search space.",
- }
Genetic Programming entries for
Janardhanan Sreekanth
Bithin Datta
Citations