The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Default user image.

Babak Mohammadi

Doctoral student

Default user image.

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

Author

  • Babak Mohammadi
  • Farshad Ahmadi
  • Saeid Mehdizadeh
  • Yiqing Guan
  • Quoc Bao Pham
  • Nguyen Thi Thuy Linh
  • Doan Quang Tri

Summary, in English

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.

Publishing year

2020-08-01

Language

English

Pages

3387-3409

Publication/Series

Water Resources Management

Volume

34

Issue

10

Document type

Journal article

Publisher

Springer

Topic

  • Water Engineering

Keywords

  • Bi-linear
  • Daily streamflow
  • Multi-layer perceptron
  • Multi-verse optimizer
  • Particle swarm optimization

Status

Published

ISBN/ISSN/Other

  • ISSN: 0920-4741