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Title: | Comparative analysis of hybrid models of firefly optimization algorithm with support vector machines and multilayer perceptron for predicting soil temperature at different depths | Authors: | Shamshirband, S Esmaeilbeiki, F Zarehaghi, D Neyshabouri, M Sannadianfard, S Ghorbani, MA Mosavi, A Nabipour, N Chau, KW |
Issue Date: | 2020 | Source: | Engineering applications of computational fluid mechanics, 2020, v. 14, no. 1, p. 939-953 | Abstract: | This research aims to model soil temperature (ST) using machine learning models of multilayer perceptron (MLP) algorithm and support vector machine (SVM) in hybrid form with the Firefly optimization algorithm, i.e. MLP-FFA and SVM-FFA. In the current study, measured ST and meteorological parameters of Tabriz and Ahar weather stations in a period of 2013-2015 are used for training and testing of the studied models with one and two days as a delay. To ascertain conclusive results for validation of the proposed hybrid models, the error metrics are benchmarked in an independent testing period. Moreover, Taylor diagrams utilized for that purpose. Obtained results showed that, in a case of one day delay, except in predicting ST at 5 cm below the soil surface (ST5(cm)) at Tabriz station, MLP-FFA produced superior results compared with MLP, SVM, and SVM-FFA models. However, for two days delay, MLP-FFA indicated increased accuracy in predicting ST(5cm)and ST(20cm)of Tabriz station and ST(10cm)of Ahar station in comparison with SVM-FFA. Additionally, for all of the prescribed models, the performance of the MLP-FFA and SVM-FFA hybrid models in the testing phase was found to be meaningfully superior to the classical MLP and SVM models. | Keywords: | Firefly optimization algorithm Soil temperature Artificial neural networks Hybrid machine learning Prediction |
Publisher: | Hong Kong Polytechnic University, Department of Civil and Structural Engineering | Journal: | Engineering applications of computational fluid mechanics | ISSN: | 1994-2060 | EISSN: | 1997-003X | DOI: | 10.1080/19942060.2020.1788644 | Rights: | © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The following publication Shamshirband, S., Esmaeilbeiki, F., Zarehaghi, D., Neyshabouri, M., Sannadianfard, S., Ghorbani, M. A., . . . Chau, K. W. (2020). Comparative analysis of hybrid models of firefly optimization algorithm with support vector machines and multilayer perceptron for predicting soil temperature at different depths. Engineering Applications of Computational Fluid Mechanics, 14(1), 939-953 is available at https://dx.doi.org/10.1080/19942060.2020.1788644 |
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Shamshirband_Hybrid_Models_Firefly.pdf | 5.16 MB | Adobe PDF | View/Open |
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