Novel hybrid approaches based on evolutionary strategy for streamfow forecasting in the Chellif River, Algeria

Zakhrouf, Mousaab; Bouchelkia, Hamid; Stamboul, Madani; Kim, Sungwon

doi:10.1007/s11600-019-00380-5

Artykuł - szczegóły

Tytuł artykułu

Novel hybrid approaches based on evolutionary strategy for streamfow forecasting in the Chellif River, Algeria

Autorzy

Zakhrouf Mousaab , Bouchelkia Hamid , Stamboul Madani , Kim Sungwon

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-019-00380-5

Warianty tytułu

Języki publikacji

Abstrakty

In this study, the feedforward neural networks (FFNNs) were proposed to forecast the multi-day-ahead streamfow. The parameters of FFNNs model were optimized utilizing genetic algorithm (GA). Moreover, discrete wavelet transform was utilized to enhance the accuracy of FFNNs model’s forecasting. Therefore, the wavelet-based feedforward neural networks (WFFNNs-GA) model was developed for the multi-day-ahead streamfow forecasting based on three evolutionary strategies [i.e., multi-input multi-output (MIMO), multi-input single-output (MISO), and multi-input several multi-output (MISMO)]. In addition, the developed models were evaluated utilizing fve diferent statistical indices including root mean squared error, signal-to-noise ratio, correlation coefcient, Nash–Sutclife efciency, and peak fow criteria. Results provided that the statistical values of WFFNNs-GA model based on MISMO evolutionary strategy were superior to those of WFFNNs-GA model based on MISO and MIMO evolutionary strategies for the multi-day-ahead streamfow forecasting. Results indicated that the performance of WFFNNs-GA model based on MISMO evolutionary strategy provided the best accuracy. Results also explained that the hybrid model suggested better performance compared with stand-alone model based on the corresponding evolutionary strategies. Therefore, the hybrid model can be an efcient and robust implement to forecast the multi-day-ahead streamfow in the Chellif River, Algeria.

Słowa kluczowe

daily streamflow forecasting wavelet transform feedforward neural networks Chellif River multi-input multi-output multi-input single-output multi-input several multi-output

prognozowanie przepływu codzienne transformata falkowa sieci neuronowe z wyprzedzeniem Wadi asz-Szalif

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2020

Tom

Vol. 68, no. 1

Strony

167--180

Opis fizyczny

Bibliogr. 71 poz.

Twórcy

autor

Zakhrouf Mousaab

mousaab@gmail.com

URMER Laboratory, Department of Hydraulic, Faculty of Technology, University of Tlemcen, Tlemcen, Algeria

autor

Bouchelkia Hamid

bouchelkiahamid@gmail.com

URMER Laboratory, Department of Hydraulic, Faculty of Technology, University of Tlemcen, Tlemcen, Algeria

autor

Stamboul Madani

stamboul_m@yahoo.fr

Research Laboratory of Water Resources, Soil and Environment, Department of Civil Engineering, Faculty of Architecture and Civil Engineering, Amar Telidji University, Laghouat, Algeria

autor

Kim Sungwon

swkim1968@dyu.ac.kr

Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju 36040, South Korea

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021)

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Bibliografia

Identyfikator YADDA

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