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Engineering with Computers

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23.01.2021 | Original Article

Quick integrative optimizers for minimizing the error of neural computing in pan evaporation modeling

verfasst von: Hossein Moayedi, Soheil Ghareh, Loke Kok Foong

Erschienen in: Engineering with Computers | Sonderheft 2/2022

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Abstract

To achieve an efficient methodology for approximating pan evaporation (E_P), this study offers two metaheuristic-integrated predictors. Shuffled complex evolution (SCE) and electromagnetic field optimization (EFO) are two of the fastest metaheuristic algorithms that are synthesized with artificial neural network (ANN). By doing this, the ANN is optimized in a noticeably shorter time compared to its integration with other metaheuristic techniques. Five-year climatic data of the Bakersfield station (California, USA) with an 80:20 ratio are used for developing and testing the methods. The proposed hybrids are implemented with appropriate population sizes (20 and 35 for the SCE and EFO, respectively) and their results are compared to a single ANN. Accuracy evaluation (correlation coefficients > 0.99) professed that the neural network with both conventional and sophisticated trainers is a competent approach for the E_P simulation. Besides, it was observed that the error of prediction by the ANN-SCE and ANN-EFO is 6.02 and 9.27% lower than the single ANN, respectively. Therefore, the used strategies can enhance the applicability of the ANN. The time elapsed in the optimization using SCE and EFO was 479.0 and 281.9 s, respectively. A comparison between these algorithms revealed that the EFO is both a faster and more accurate optimizer. The ANN-EFO is accordingly recommended as a new efficient model for predicting the E_P.

Vorheriger Artikel Axial magnetic field effect on wave propagation in bi-layer FG graphene platelet-reinforced nanobeams

Nächster Artikel Credibility distribution function based global and regional sensitivity analysis under fuzzy uncertainty

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Titel: Quick integrative optimizers for minimizing the error of neural computing in pan evaporation modeling
verfasst von: Hossein Moayedi
Soheil Ghareh
Loke Kok Foong
Publikationsdatum: 23.01.2021
Verlag: Springer London
Erschienen in: Engineering with Computers / Ausgabe Sonderheft 2/2022
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI: https://doi.org/10.1007/s00366-020-01277-4

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