Groundwater level simulation and forecasting using interior search algorithm-least square support vector regression (ISA-LSSVR)
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @Article{MORAVEJ:2020:GSD,
-
author = "Mojtaba Moravej and Pouria Amani and
Seyed-Mohammad Hosseini-Moghari",
-
title = "Groundwater level simulation and forecasting using
interior search algorithm-least square support vector
regression ({ISA-LSSVR})",
-
journal = "Groundwater for Sustainable Development",
-
volume = "11",
-
pages = "100447",
-
year = "2020",
-
ISSN = "2352-801X",
-
DOI = "doi:10.1016/j.gsd.2020.100447",
-
URL = "http://www.sciencedirect.com/science/article/pii/S2352801X20302411",
-
keywords = "genetic algorithms, genetic programming, Data-driven
methods, Optimization, Karaj aquifer, Sensitivity
analysis, Support vector machines",
-
abstract = "Least square support vector regression (LSSVR) is a
powerful data-driven method for simulation and
forecasting, with two parameters to tune. In this
study, these parameters were automatically tuned using
the interior search algorithm (ISA) and genetic
algorithm (GA). The main purpose is in situ simulation
and forecast of monthly groundwater level in Karaj
plain, Iran, using historical groundwater level,
precipitation, and evaporation data. The results of the
interior search algorithm-least support vector
regression (ISA-LSSVR) and genetic algorithm-least
support vector regression (GA-LSSVR) compared with
genetic programming (GP) and adaptive neural fuzzy
inference system (ANFIS). Based on average
Nash-Sutcliffe criterion, the results revealed that the
ISA-LSSVR improves the simulation and forecasting
accuracy compared to other methods. Also, the results
of the different model structure selection indicate
that including precipitation and evaporation does not
necessarily improve simulation and forecasting
accuracy, but it would increase uncertainty. This
increase suggests that groundwater level in the case
study is affected by groundwater flow, recharge from
leaky urban water infrastructure, and reduced recharge
from precipitation due to impervious surfaces in urban
areas rather than being solely governed by
precipitation and evaporation. Finally, a sensitivity
analysis was performed to assess the impacts of
optimization algorithm parameters on the simulation and
forecasting accuracy. The results indicate high and low
sensitivity associated with GA and ISA, respectively.
In conclusion, ISA-LSSVR was suggested as the best
model due to computational efficiency, low sensitivity
to its parameters, and high accuracy compared to other
methods",
- }
Genetic Programming entries for
Mojtaba Moravej
Pouria Amani
Seyed-Mohammad Hosseini-Moghari
Citations