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

• Main Authors: Shahaboddin Shamshirband, Fatemeh Esmaeilbeiki, Davoud Zarehaghi, Mohammadreza Neyshabouri, Saeed Samadianfard, Mohammad Ali Ghorbani, Amir Mosavi, Narjes Nabipour, Kwok-Wing Chau
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2020-01-01
• Series: Engineering Applications of Computational Fluid Mechanics
• Subjects: firefly optimization algorithm; soil temperature; artificial neural networks; hybrid machine learning; prediction
• Online Access: http://dx.doi.org/10.1080/19942060.2020.1788644

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.