> Details

Mosavi, Amir [Author]; Shamshirband, Shahaboddin [Author]; Esmaeilbeiki, Fatemeh [Author]; Zarehaghi, Davoud [Author]; Neyshabouri, Mohammadreza [Author]; Samadianfard, Saeed [Author]; Ghorbani, Mohammad Ali [Author]; Nabipour, Narjes [Author]; Chau, Kwok-Wing [Author]

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

Reference
management

Bookmarks

Remove from
bookmarks

Share this on Whatsapp

Close

Bookmarks



You can manage bookmarks using lists, please log in to your user account for this.
  • Media type: E-Article
  • Title: Comparative analysis of hybrid models of firefly optimization algorithm with support vector machines and multilayer perceptron for predicting soil temperature at different depths
  • Contributor: Mosavi, Amir [Author]; Shamshirband, Shahaboddin [Author]; Esmaeilbeiki, Fatemeh [Author]; Zarehaghi, Davoud [Author]; Neyshabouri, Mohammadreza [Author]; Samadianfard, Saeed [Author]; Ghorbani, Mohammad Ali [Author]; Nabipour, Narjes [Author]; Chau, Kwok-Wing [Author]
  • imprint: Publication Server of Weimar Bauhaus-University / Online-Publikations-System der Bauhaus-Universität Weimar, 2020-07-10
  • Language: English
  • Keywords: artificial neural networks ; Algorithmus ; hybrid machine learning ; bk:54 ; Neuronales Netz ; Bodentemperatur ; Maschinelles Lernen ; OA-Publikationsfonds2019 ; soil temperature ; firefly optimization algorithm
  • Origination:
  • Footnote: Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
  • Description: 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 (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.
  • Access State: Open Access