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Viability of the advanced adaptive neuro-fuzzy inference system model on reservoir evaporation process simulation: case study of Nasser Lake in Egypt

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journal contribution
posted on 2019-01-01, 00:00 authored by S Q Salih, M F Allawi, A A Yousif, A M Armanuos, M K Saggi, Mumtaz Ali, S Shahid, N Al-Ansari, Z M Yaseen, K W Chau
© 2019, © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Reliable prediction of evaporative losses from reservoirs is an essential component of reservoir management and operation. Conventional models generally used for evaporation prediction have a number of drawbacks as they are based on several assumptions. A novel approach called the co-active neuro-fuzzy inference system (CANFIS) is proposed in this study for the modeling of evaporation from meteorological variables. CANFIS provides a center-weighted set rather than global weight sets for predictor–predictand relationship mapping and thus it can provide a higher prediction accuracy. In the present study, adjustments are made in the back-propagation algorithm of CANFIS for automatic updating of membership rules and further enhancement of its prediction accuracy. The predictive ability of the CANFIS model is validated with three well-established artificial intelligence (AI) models. Different statistical metrics are computed to investigate the prediction efficacy. The results reveal higher accuracy of the CANFIS model in predicting evaporation compared to the other AI models. CANFIS is found to be capable of modeling evaporation from mean temperature and relative humidity only, with a Nash–Sutcliffe efficiency of 0.93, which is much higher than that of the other models. Furthermore, CANFIS improves the prediction accuracy by 9.2–55.4% compared to the other AI models.

History

Journal

Engineering Applications of Computational Fluid Mechanics

Volume

13

Issue

1

Pagination

878 - 891

Publisher

Taylor & Francis

Location

London, Eng.

ISSN

1994-2060

eISSN

1997-003X

Language

eng

Publication classification

C Journal article; C1 Refereed article in a scholarly journal