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

References

• Beltrami, H. (2001). On the relationship between ground temperature histories and meteorological records: A report on the Pomquet station. Global and Planetary Change, 29(3-4), 327–348. https://doi.org/10.1016/S0921-8181(01)00098-4
   Web of Science ®Google Scholar
• Bilgili, M. (2010). Prediction of soil temperature using regression and artificial neural network models. Meteorology and Atmospheric Physics, 110(1–2), 59–70. https://doi.org/10.1007/s00703-010-0104-x
   Web of Science ®Google Scholar
• Bilgili, M. (2011). The use of artificial neural networks for forecasting the monthly mean soil temperatures in Adana, Turkey. Turkish Journal of Agriculture and Forestry, 35(1), 83–93. doi:10.3906/tar-1001-593.
   Web of Science ®Google Scholar
• Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 121–167. https://doi.org/10.1023/A:1009715923555
   Web of Science ®Google Scholar
• Chau, K. W., & Muttil, N. (2007). Data mining and multivariate statistical analysis for ecological system in coastal waters. Journal of Hydroinformatics, 9(4), 305–317. https://doi.org/10.2166/hydro.2007.003
   Web of Science ®Google Scholar
• Cheng, C. T., Lin, J. Y., Sun, Y. G., & Chau, K. W. (2005). Long-term prediction of discharges in Manwan Hydropower using adaptive-network-based fuzzy inference systems models. Lecture Notes in Computer Science, 3612, 1152–1161. https://doi.org/10.1007/11539902_145
   Google Scholar
• Citakoglu, H. (2017). Comparison of artificial intelligence techniques for prediction of soil temperatures in Turkey. Theoretical and Applied Climatology, 130(1-2), 545–556. https://doi.org/10.1007/s00704-016-1914-7
   Web of Science ®Google Scholar
• Deo, R. C., & Sahin, M. (2015). Application of the Artificial neural network model for prediction of monthly standardized precipitation and evapotranspiration index using hydrometeorological parameters and climate indices in eastern Australia. Atmospheric Research, 161–162, 65–81. https://doi.org/10.1016/j.atmosres.2015.03.018
   Web of Science ®Google Scholar
• Deo, R. C., & Sahin, M. (2017). Forecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in Queensland. Renewable and Sustainable Energy Reviews, 72, 828–848. https://doi.org/10.1016/j.rser.2017.01.114
   Web of Science ®Google Scholar
• Firat, M., & Gungor, M. (2009). Generalized regression neural networks and feed forward neural networks for prediction of scour depth around bridge piers. Advances in Engineering Software, 40(8), 731–737. https://doi.org/10.1016/j.advengsoft.2008.12.001
   Web of Science ®Google Scholar
• Fister, I., Fister Jr., I., Yang, I. X. S., & Brest, J. (2013). A comprehensive review of firefly algorithms. Swarm and Evolutionary Computation, 13, 34–46. https://doi.org/10.1016/j.swevo.2013.06.001
   Web of Science ®Google Scholar
• Fotovatikhah, F., Herrera, M., Shamshirband, S., Chau, K. W., Faizollahzadeh Ardabili, S., & Piran, J. (2018). Survey of computational intelligence as basis to big flood management: Challenges, research directions and future work. Engineering Applications of Computational Fluid Mechanics, 12(1), 411–437. https://doi.org/10.1080/19942060.2018.1448896
   Web of Science ®Google Scholar
• Gazi, V., & Passino, K. M. (2004). Stability analysis of social foraging swarms. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 34(1), 539–557. https://doi.org/10.1109/TSMCB.2003.817077
   PubMed Web of Science ®Google Scholar
• Ghorbani, M. A., Ahmadzadeh, H., Isazadeh, M., & Terzi, O. (2016). A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction. Environmental Earth Sciences, 75(6), 476–490. https://doi.org/10.1007/s12665-015-5096-x
   Web of Science ®Google Scholar
• Ghorbani, M. A., Shamshirband, S., Zare Haghi, D., Azani, A., Bonakdari, H., & Ebtehaj, I. (2017). Application of firefly algorithm-based support vector machines for prediction of field capacity and permanent wilting point. Soil and Tillage Research, 172, 32–38. https://doi.org/10.1016/j.still.2017.04.009
   Web of Science ®Google Scholar
• Gill, M. K., Asefa, T., Kemblowski, M. W., & McKee, M. (2006). Soil moisture prediction using support vector machines. Journal of the American Water Resources Association, 42(4), 1033–1046. https://doi.org/10.1111/j.1752-1688.2006.tb04512.x
   Web of Science ®Google Scholar
• Gleckler, P. J., Taylor, K. E., & Doutriaux, C. (2008). Performance metrics for climate models. Journal of Geophysical Research, 113(D6), 1–20. https://doi.org/10.1029/2007JD008972
   Web of Science ®Google Scholar
• Hanks, R. J., Austin, D. D., & Ondrechan, W. T. (1971). Soil temperature estimation by a numerical method. Proceedings Soil Science Society of America, 35, 665–667. doi:10.2136/sssaj1971.03615995003500050015x.
   Web of Science ®Google Scholar
• Hillel, D. (1998). Environmental soil physics (p. 771). Academic Press.
   Google Scholar
• Kazemi, S. M. R., Minaei Bidgoli, B., Shamshirband, S., Karimi, S. M., Ghorbani, M. A., Chau, K. W., & Kazem Pour, R. (2018). Novel genetic-based negative correlation learning for estimating soil temperature. Engineering Applications of Computational Fluid Mechanics, 12(1), 506–516. https://doi.org/10.1080/19942060.2018.1463871
   Web of Science ®Google Scholar
• Kim, S., & Singh, V. P. (2014). Modeling daily soil temperature using data-driven models and spatial distribution. Theoretical and Applied Climatology, 118(3), 465–479. https://doi.org/10.1007/s00704-013-1065-z
   Web of Science ®Google Scholar
• Kisi, O., Tombul, M., & Kermani, M. Z. (2015). Modeling soil temperatures at different depths by using three different neural computing techniques. Theoretical and Applied Climatology, 121(1-2), 377–387. https://doi.org/10.1007/s00704-014-1232-x
   Web of Science ®Google Scholar
• Kurup, P. U., & Dudani, N. K. (2002). Neural networks for profiling stress history of clays from PCPT data. Journal of Geotechnical and Geoenvironmental Engineering, 128(7), 569–579. https://doi.org/10.1061/(ASCE)1090-0241(2002)128:7(569)
   Web of Science ®Google Scholar
• Mazou, E., Alvertos, N., & Tsiros, I. X. (2013). Soil temperature prediction using time-delay neural networks. Advances in Meteorology, Climatology and Atmospheric Physics, 611–615. https://doi.org/10.1007/978-3-642-29172-2_87
   Google Scholar
• McClelland, J., & Rumelhart, D. (1988). Explorations in parallel distributed processing: A handbook of models, programs, and exercises. MIT Press.
   Google Scholar
• Mehdizadeh, M., Behmanesh, J., & Khalili, K. (2018). Comprehensive modeling of monthly mean soil temperature using multivariate adaptive regression splines and support vector machine. Theoretical and Applied Climatology, 133(3-4), 911–924. https://doi.org/10.1007/s00704-017-2227-1
   Web of Science ®Google Scholar
• Moazenzadeh, R., Mohammadi, B., Shamshirband, S., & Chau, K. W. (2018). Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran. Engineering Applications of Computational Fluid Mechanics, 12(1), 584–597. https://doi.org/10.1080/19942060.2018.1482476
   Web of Science ®Google Scholar
• Pal, M. (2006). Support vector machines-based modelling of seismic liquefaction potential. International Journal for Numerical and Analytical Methods in Geomechanics, 30(10), 983–996. https://doi.org/10.1002/nag.509
   Web of Science ®Google Scholar
• Plauborg, F. (2002). Simple model for 10 cm soil temperature in different soils with short grass. European Journal of Agronomy, 17(3), 173–179. https://doi.org/10.1016/S1161-0301(02)00006-0
   Web of Science ®Google Scholar
• Qasem, S. N., Samadianfard, S., Kheshtgar, S., Jarhan, S., Kisi, O., Shamshirband, S., & Chau, K. W. (2019). Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates. Engineering Applications of Computational Fluid Mechanics, 13(1), 177–187. https://doi.org/10.1080/19942060.2018.1564702
   Web of Science ®Google Scholar
• Qian, B., Gregorich, E. G., Gameda, S., Hopkins, D. W., & Wang, X. L. (2011). Observed soil temperature trends associated with climate change in Canada. Journal of Geophysical Research, 116(D2), 1–16. https://doi.org/10.1029/2010JD015012
   Web of Science ®Google Scholar
• Sabziparvar, A. A., Tabari, H., & Aeini, A. (2010). Estimation of mean daily soil temperature by means of meteorological data in some selected climates of Iran. Water And Soil Science (Journal Of Science and Technology of Agriculture and Natural Resources), 14(52), 125–137. (In Persian). http://jstnar.iut.ac.ir/article-1-1229-en.html.
   Google Scholar
• Sabziparvar, A. A., Zare Abyaneh, H., & Bayat Varkeshi, M. (2010). A model comparison between predicted soil temperatures using ANFIS model and regression methods in three different climates. Journal of Water and Soil (Agricultural Sciences and Technology), 24(2), 274–285. (In Persian). https://www.sid.ir/en/journal/ViewPaper.aspx?ID=177202.
   Google Scholar
• Samadianfard, S., Asadi, E., Jarhan, S., Kazemi, H., Kheshtgar, S., Kisi, O., Sajjadi, S., & Abdul Manaf, A. (2018). Wavelet neural networks and gene expression programming models to predict short-term soil temperature at different depths. Soil and Tillage Research, 175, 37–50. https://doi.org/10.1016/j.still.2017.08.012
   Web of Science ®Google Scholar
• Samadianfard, S., Delirhasannia, R., Kisi, O., & Agirre-Basurko, E. (2013). Comparative analysis of ozone level prediction models using gene expression programming and multiple linear regression. Geofizika, 30(1), 43–74. https://hrcak.srce.hr/105853.
   Web of Science ®Google Scholar
• Samadianfard, S., Ghorbani, M. A., & Mohammadi, B. (2018). Forecasting soil temperature at multiple-depth with a hybrid artificial neural network model coupled-hybrid firefly optimizer algorithm. Information Processing in Agriculture, 5, 465–476. https://doi.org/10.1016/j.inpa.2018.06.005
   Google Scholar
• Samadianfard, S., Majnooni-Heris, A., Qasem, S. N., Kisi, O., Shamshirband, S., & Chau, K. W. (2019). Daily global solar radiation modeling using data-driven techniques and empirical equations in a semi-arid climate. Engineering Applications of Computational Fluid Mechanics, 13(1), 142–157. https://doi.org/10.1080/19942060.2018.1560364
   Web of Science ®Google Scholar
• Samadianfard, S., Sattari, M. T., Kisi, O., & Kazemi, H. (2014). Determining flow friction factor in irrigation pipes using data mining and artificial intelligence approaches. Applied Artificial Intelligence, 28(8), 793–813. https://doi.org/10.1080/08839514.2014.952923
   Web of Science ®Google Scholar
• Sanikhani, H., Deo, R. C., Yaseen, Z. M., Eray, O., & Kisi, O. (2018). Non-tuned data intelligent model for soil temperature estimation: A new approach. Geoderma, 330, 52–64. https://doi.org/10.1016/j.geoderma.2018.05.030
   Web of Science ®Google Scholar
• Sihag, P., Esmaeilbeiki, F., Singh, B., & MuhammedPandhiani, S. (2019). Model-based soil temperature estimation using climatic parameters: The case of Azerbaijan province, Iran. Geology, Ecology, and Landscapes. https://doi.org/10.1080/24749508.2019.1610841
   Google Scholar
• Tabari, H., Sabziparvar, A. A., & Ahmadi, M. (2011). Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region. Meteorology and Atmospheric Physics, 110(3–4), 135–142. https://doi.org/10.1007/s00703-010-0110-z
   Web of Science ®Google Scholar
• Tabari, H., Talaee, P. H., & Willems, P. (2015). Short-term forecasting of soil temperature using artificial neural network. Meteorological Applications, 22(3), 576–585. https://doi.org/10.1002/met.1489
   Web of Science ®Google Scholar
• Taylor, K. E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106(D7), 7183–7192. https://doi.org/10.1029/2000JD900719
   Web of Science ®Google Scholar
• Ustaoglu, B., Cigizoglu, H. K., & Karaca, M. (2008). Forecast of daily mean, maximum and minimum temperature time series by three artificial neural network methods. Meteorological Applications, 15(4), 431–445. https://doi.org/10.1002/met.83
   Web of Science ®Google Scholar
• Vapnik, V. N. (1995). The nature of statistical learning theory. Springer-Verlag. ISBN 0-387-98780-0.
   Google Scholar
• Wu, W., Tang, X. P., Guo, N. J., Yang, C., Liu, H. B., & Shang, Y. F. (2013). Spatiotemporal modeling of monthly soil temperature using artificial neural networks. Theoretical and Applied Climatology, 113(3-4), 481–494. https://doi.org/10.1007/s00704-012-0807-7
   Web of Science ®Google Scholar
• Yang, X. S. (2008). Introduction to mathematical optimization. In From linear programming to metaheuristics. Cambridge, UK: Cambridge International Science Publishing. ISBN 978-1-904602-82-8. https://www.waterstones.com/book/introduction-to-mathematical-optimization/xin-she-yang/9781904602828.
   Google Scholar
• Yaseen, Z. M., Sulaiman, S. O., Deo, R. C., & Chau, K. W. (2019). An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction. Journal of Hydrology, 569, 387–408. https://doi.org/10.1016/j.jhydrol.2018.11.069
   Web of Science ®Google Scholar
• Zheng, D., Hunt Jr., E. R., & Running, S. W. (1993). A daily soil temperature model based on air temperature and precipitation for continental applications. Climate Research, 2(3), 183–191. https://doi.org/10.3354/cr002183
   Google Scholar