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Developing Novel Robust Models to Improve the Accuracy of Daily Streamflow Modeling

Mohammadi, Babak LU orcid ; Ahmadi, Farshad ; Mehdizadeh, Saeid ; Guan, Yiqing ; Pham, Quoc Bao ; Linh, Nguyen Thi Thuy and Tri, Doan Quang (2020) In Water Resources Management 34(10). p.3387-3409
Abstract

Streamflow plays a major role in the optimal management and allocation of available water resources in each region. Reliable techniques are therefore needed to be developed for streamflow modeling. In the present study, the performance of streamflow modeling is improved via developing novel boosted models. The daily streamflows of four hydrometric stations comprising of the Brantford and Galt stations located on the Grand River, Canada, as well as Macon and Elkton stations respectively, located on the Ocmulgee and Umpqua rivers, United States, are used. Three different types of boosted models are implemented and proposed by coupling the classical multi-layer perceptron (MLP) with the optimization algorithms, including particle swarm... (More)

Streamflow plays a major role in the optimal management and allocation of available water resources in each region. Reliable techniques are therefore needed to be developed for streamflow modeling. In the present study, the performance of streamflow modeling is improved via developing novel boosted models. The daily streamflows of four hydrometric stations comprising of the Brantford and Galt stations located on the Grand River, Canada, as well as Macon and Elkton stations respectively, located on the Ocmulgee and Umpqua rivers, United States, are used. Three different types of boosted models are implemented and proposed by coupling the classical multi-layer perceptron (MLP) with the optimization algorithms, including particle swarm optimization (PSO) and coupled particle swarm optimization-multi-verse optimizer (PSOMVO) and a time series model, namely the bi-linear (BL). So, the boosted MLP-PSO, MLP-PSOMVO, and MLP-BL models are developed. The accuracy of all the boosted models is compared with the classical MLP and BL by the statistical metrics used. It is concluded that all the boosted models developed at the studied stations lead to superior modeling results of the daily streamflows to the classical MLP; however, the boosted MLP-BL models generally outperformed the MLP-PSO and MLP-PSOMVO ones.

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author
; ; ; ; ; and
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Bi-linear, Daily streamflow, Multi-layer perceptron, Multi-verse optimizer, Particle swarm optimization
in
Water Resources Management
volume
34
issue
10
pages
23 pages
publisher
Springer
external identifiers
  • scopus:85087883264
ISSN
0920-4741
DOI
10.1007/s11269-020-02619-z
language
English
LU publication?
no
id
7ee99fef-8355-4f28-a866-0d3eb35d7320
date added to LUP
2020-12-30 05:14:08
date last changed
2022-04-26 22:52:49
@article{7ee99fef-8355-4f28-a866-0d3eb35d7320,
  abstract     = {{<p>Streamflow plays a major role in the optimal management and allocation of available water resources in each region. Reliable techniques are therefore needed to be developed for streamflow modeling. In the present study, the performance of streamflow modeling is improved via developing novel boosted models. The daily streamflows of four hydrometric stations comprising of the Brantford and Galt stations located on the Grand River, Canada, as well as Macon and Elkton stations respectively, located on the Ocmulgee and Umpqua rivers, United States, are used. Three different types of boosted models are implemented and proposed by coupling the classical multi-layer perceptron (MLP) with the optimization algorithms, including particle swarm optimization (PSO) and coupled particle swarm optimization-multi-verse optimizer (PSOMVO) and a time series model, namely the bi-linear (BL). So, the boosted MLP-PSO, MLP-PSOMVO, and MLP-BL models are developed. The accuracy of all the boosted models is compared with the classical MLP and BL by the statistical metrics used. It is concluded that all the boosted models developed at the studied stations lead to superior modeling results of the daily streamflows to the classical MLP; however, the boosted MLP-BL models generally outperformed the MLP-PSO and MLP-PSOMVO ones.</p>}},
  author       = {{Mohammadi, Babak and Ahmadi, Farshad and Mehdizadeh, Saeid and Guan, Yiqing and Pham, Quoc Bao and Linh, Nguyen Thi Thuy and Tri, Doan Quang}},
  issn         = {{0920-4741}},
  keywords     = {{Bi-linear; Daily streamflow; Multi-layer perceptron; Multi-verse optimizer; Particle swarm optimization}},
  language     = {{eng}},
  month        = {{08}},
  number       = {{10}},
  pages        = {{3387--3409}},
  publisher    = {{Springer}},
  series       = {{Water Resources Management}},
  title        = {{Developing Novel Robust Models to Improve the Accuracy of Daily Streamflow Modeling}},
  url          = {{http://dx.doi.org/10.1007/s11269-020-02619-z}},
  doi          = {{10.1007/s11269-020-02619-z}},
  volume       = {{34}},
  year         = {{2020}},
}