Assessment of Artificial Intelligence Models for Developing Single-Value and Loop Rating Curves
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Niazkar:2021:Complexity,
-
author = "Majid Niazkar and Mohammad Zakwan",
-
title = "Assessment of Artificial Intelligence Models for
Developing Single-Value and Loop Rating Curves",
-
journal = "Complexity",
-
year = "2021",
-
note = "Special Issue: Solving Complex Hydrological Processes
using Advanced Artificial Intelligence Models",
-
keywords = "genetic algorithms, genetic programming",
-
bibsource = "OAI-PMH server at oai.repec.org",
-
identifier = "RePEc:hin:complx:6627011",
-
oai = "oai:RePEc:hin:complx:6627011",
-
URL = "https://downloads.hindawi.com/journals/complexity/2021/6627011.pdf",
-
DOI = "doi:10.1155/2021/6627011",
-
size = "21 pages",
-
abstract = "Estimation of discharge flowing through rivers is an
important aspect of water resource planning and
management. The most common way to address this concern
is to develop stage-discharge relationships at various
river sections. Various computational techniques have
been applied to develop discharge ratings and improve
the accuracy of estimated discharges. In this regard,
the present study explores the application of the novel
hybrid multigene genetic programming-generalised
reduced gradient (MGGP-GRG) technique for estimating
river discharges for steady as well as unsteady flows.
It also compares the MGGP-GRG performance with those of
the commonly used optimisation techniques. As a result,
the rating curves of eight different rivers were
developed using the conventional method, evolutionary
algorithm (EA), the modified honey bee mating
optimisation (MHBMO) algorithm, artificial neural
network (ANN), MGGP, and the hybrid MGGP-GRG technique.
The comparison was conducted on the basis of several
widely used performance evaluation criteria. It was
observed that no model outperformed others for all
datasets and metrics considered, which demonstrates
that the best method may be different from one case to
another one. Nevertheless, the ranking analysis
indicates that the hybrid MGGP-GRG model overall
performs the best in developing stage-discharge
relationships for both single-value and loop rating
curves. For instance, the hybrid MGGP-GRG technique
improved sum of square of errors obtained by the
conventional method between 4.5percent and 99percent
for six out of eight datasets. Furthermore, EA, the
MHBMO algorithm, and artificial intelligence (AI)
models (ANN and MGGP) performed satisfactorily in some
of the cases, while the idea of combining MGGP with GRG
reveals that this hybrid method improved the
performance of MGGP in this specific application.
Unlike the black box nature of ANN, MGGP offers
explicit equations for stream rating curves, which may
be counted as one of the advantages of this AI model.",
-
notes = "Department of Civil and Environmental Engineering,
Shiraz University, Shiraz, Iran",
- }
Genetic Programming entries for
Majid Niazkar
Mohammad Zakwan
Citations