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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
Institute of Research and Development, Duy Tan University, Da Nang, Vietnam.
Department of Civil Engineering, Al-Esraa University College, Baghdad, Iraq.
Water Resources Engineering Department, College of Engineering, University of Duhok, Duhok, Iraq.
Irrigation and Hydraulics Engineering Department, Civil Engineering Department, Faculty of Engineering, Tanta University, Tanta, Egypt.
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2019 (English)In: Engineering Applications of Computational Fluid Mechanics, ISSN 1994-2060, E-ISSN 1997-003X, Vol. 13, no 1, p. 878-891Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
UK: Taylor & Francis, 2019. Vol. 13, no 1, p. 878-891
Keywords [en]
reservoir operation, evaporation prediction, artificial intelligent models, CANFIS, arid environment
National Category
Engineering and Technology Geotechnical Engineering
Research subject
Soil Mechanics
Identifiers
URN: urn:nbn:se:ltu:diva-75461DOI: 10.1080/19942060.2019.1647879ISI: 000480244200001OAI: oai:DiVA.org:ltu-75461DiVA, id: diva2:1341630
Note

Validerad;2019;Nivå 2;2019-08-13 (johcin)

Available from: 2019-08-09 Created: 2019-08-09 Last updated: 2019-08-30Bibliographically approved

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