Comparative analysis of hybrid models of firefly optimization algorithm with support vector machines and multilayer perceptron for predicting soil temperature at different depths

  • 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 ofThis 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 (ST5cm) 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 ST5cm and ST 20cm of Tabriz station and ST10cm 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.show moreshow less

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Metadaten
Document Type:Article
Author:Dr. Amir MosaviORCiD, Shahaboddin ShamshirbandORCiD, Fatemeh Esmaeilbeiki, Davoud Zarehaghi, Mohammadreza Neyshabouri, Saeed Samadianfard, Mohammad Ali Ghorbani, Narjes Nabipour, Kwok-Wing Chau
DOI (Cite-Link):https://doi.org/10.1080/19942060.2020.1788644Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20200911-42347Cite-Link
URL:https://www.tandfonline.com/doi/full/10.1080/19942060.2020.1788644
Parent Title (English):Engineering Applications of Computational Fluid Mechanics
Language:English
Date of Publication (online):2020/09/10
Date of first Publication:2020/07/10
Release Date:2020/09/11
Publishing Institution:Bauhaus-Universität Weimar
Institutes:Fakultät Bauingenieurwesen / Institut für Strukturmechanik
Volume:2020
Issue:Volume 14, Issue 1
Pagenumber:15
First Page:939
Last Page:953
Tag:artificial neural networks; firefly optimization algorithm; hybrid machine learning; soil temperature
GND Keyword:Bodentemperatur; Algorithmus; Maschinelles Lernen; Neuronales Netz
Dewey Decimal Classification:000 Informatik, Informationswissenschaft, allgemeine Werke
BKL-Classification:54 Informatik
Open Access Publikationsfonds:Open-Access-Publikationsfonds 2019
Licence (German):License Logo Creative Commons 4.0 - Namensnennung (CC BY 4.0)