Comparing ANFIS and integrating algorithm models (ICA-ANN, PSO-ANN, and GA-ANN) for prediction of energy consumption for irrigation land leveling

References

• Abbaspourgilandeh, Y., Khalilian, A., Alimardani, R., Keyhani, A. R., & Sadati, S. H. (2006). A comparison of energy requirements of uniform-depth and variable-depth tillage as affected by travel speed and soil moisture. Iranian Journal of Agricultural Sciences, 37, 573–583.
   Google Scholar
• Abdechiri, M., Faez, K., & Bahrami, H. (2010). Adaptive imperialist competitive algorithm (AICA). In 2010 9th IEEE international conference on cognitive informatics (ICCI) (pp. 940–945). IEEE.
   Google Scholar
• Abdi, B., Mozafari, H., Ayob, A., & Kohandel, R. (2011). Imperialist competitive algorithm and its application in optimization of laminated composite structures. European Journal of Scientific Research, 2, 174–187.
   Google Scholar
• Ahmadi, M. A., Ahmadi, M. R., & Shadizadeh, S. R. (2013). Evolving artificial neural network and imperialist competitive algorithm for prediction permeability of the reservoir. Neural Computing and Applications, 13(2), 1–9. doi:10.1016/j.asoc.2012.10.009
   Web of Science ®Google Scholar
• Akbarnia, A., Mohammadi, A., Farhani, F., & Alimardani, R. (2014). Simulation of draft force of winged share tillage tool using artificial neural network model. Agricultural Engineering International: CIGR Journal, 16, 57–65.
   Google Scholar
• Al-Hamed, S. A., Wahby, M. F., Al-Saqer, S. M., Aboukarima, A. M., & Sayedahmed, A. A. (2013). Artificial neural network model for predicting draft and energy requirements of a disk plow. Journal of Animal and Plant Sciences, 23(6), 1714–1724.
   Web of Science ®Google Scholar
• Avci, E. (2008). Comparison of wavelet families for texture classification by using wavelet packet entropy adaptive network based fuzzy inference system. Applied Soft Computing, 8, 225–231.10.1016/j.asoc.2007.01.003
   Web of Science ®Google Scholar
• Azadeh, A., Ghaderi, S. F., & Sohrabkhani, S. (2008). Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors. Energy Conversion and Management, 49, 2272–2278.10.1016/j.enconman.2008.01.035
   Web of Science ®Google Scholar
• Buragohain, M., & Mahanta, C. (2008). A novel approach for ANFIS modelling based on full factorial design. Applied Soft Computing, 8, 609–625.10.1016/j.asoc.2007.03.010
   Web of Science ®Google Scholar
• Calderón, A., Barrios, V., Escobar, C., Ferrer, E., Barrios, S., González-Pedel, V., … Navarro-Cid, J. (2010). Detection of left ventricular hypertrophy by different electrocardiographic criteria in clinical practice. Findings From the Sara study. Clinical and Experimental Hypertension, 32, 145–153. doi:10.1007/s00380-009
   PubMed Web of Science ®Google Scholar
• Chang, F. J., & Chang, Y. T. (2006). Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Advances in Water Resources, 29(1), 1. doi:10.1016/j.advwatres.2005.04.015
   Web of Science ®Google Scholar
• Chen, S. H., Lin, Y. H., Chang, L. C., & Chang, F. J. (2006). The strategy of building a flood forecast model by neuro-fuzzy network. Hydrological Processes, 20, 1525–1540. doi:10.1002/hyp.5942
   Web of Science ®Google Scholar
• Diamantopoulou, N. M. J. (2005). Artificial neural networks as an alternative tool in pine bark volume estimation. Computers and Electronics in Agriculture, 48, 235–244.10.1016/j.compag.2005.04.002
   Web of Science ®Google Scholar
• Ebrahimzadeh, A., Addeh, J., & Rahmani, Z. (2012). Control chart pattern recognition using K-MICA clustering and neural networks. ISA Transactions, 51, 111–119. doi:10.1016/j.isatra.2011.08.005
   PubMed Web of Science ®Google Scholar
• Jaliliantabar, F., Rabbani, H., Lorestani, A., Javadikia, P., & Gholami, R. (2013). Noise evaluation of MF285 and U650 tractors by using Adaptive Neuro-Fuzzy Inference Systems (ANFIS) method. Journal of Agronomy and Crop Science, 5, 741.
   Google Scholar
• Jang, J. R. S. (1991). Fuzzy modeling using generalized neural networks and Kalman filter algorithm. Proceedings of the Ninth National Conference on Artificial Intelligence (AAA), 91, 762–767.
   Google Scholar
• Jat, M. L., Gupta, R. K., & Rodomiro, R. S. (2006). Diversifying the intensive cereal cropping systems of the Indo- Ganges through Horticulture. Chronica Horticulturae, 46, 27–31.
   Google Scholar
• Kaveh, A., & Talatahari, S. (2010). Optimum design of skeletal structures using imperialist competitive algorithm. Computers and Structures, 88, 1220–1229.10.1016/j.compstruc.2010.06.011
   Web of Science ®Google Scholar
• Kisi, O., & Shiri, J. (2013). Prediction of long-term monthly air temperature using geographical inputs. International Journal of Climatology, 34, 179–186. doi:10.1002/joc.3676
   Web of Science ®Google Scholar
• McFarlane, B. L., Stump-Allen, R. C. G., & Watson, D. O. (2006). Public perceptions of natural disturbance in Canada’s national parks: The case of the mountain pine beetle (Dendroctonus ponderosae Hopkins). Biological Conservation, 130, 340–348. doi:10.1016/j.biocon.2005.12.029
   Web of Science ®Google Scholar
• Mohammadi, A., Rafiee, S., Keyhani, A., & Emam-Djomeh, Z. (2009). Modeling of kiwifruit (cv.Hayward) slices drying using artificial neural network. 4th International Conference on Energy Efficiency and Agricultural Engineering Rousse, Bulgaria, 1–3, pp. 397–404.
   Google Scholar
• Moumenis, B., Golmai, H., & Abbas Palangi, J. (2013). Comparison of using different systems of artificial intelligence in subsurface water level prediction (Case study: Paddy fields of plain areas between Tajin and Nectarous Rivers, Mazandaran, Iran). The Journal of Novel Applied Sciences, 2–9, 375–381.
   Google Scholar
• Movagharnejad, G. K., & Nikzad, M. (2007). Modeling of tomato drying using artificial neural network. Computers and Electronics in Agriculture, 59, 78–85.10.1016/j.compag.2007.05.003
   Web of Science ®Google Scholar
• Nagy, H. M., Watanabe, K. A., & Hirano, M. (2002). Prediction of sediment load concentration in rivers using artificial neural network model. Journal of Hydraulic Engineering, 128, 588–595.10.1061/(ASCE)0733-9429(2002)128:6(588)
   Web of Science ®Google Scholar
• Nazari-Shirkouhi, S., Eivazy, H., Ghodsi, R., Rezaie, K., & Atashpaz-Gargari, E. (2010). Solving the integrated product mix-outsourcing problem using the Imperialist Competitive Algorithm. Expert Systems with Applications, 37, 7615–7626. doi:10.1016/j.eswa.2010.04.081
   Web of Science ®Google Scholar
• Rajabioun, R., Atashpaz-Gargari, E., & Lucas, C. (2008). Colonial competitive algorithm as a tool for Nash equilibrium point achievement. Journal of Computer Sciences and Applications, 2008, 680–695. doi:10.1007/978-3-540-69848-7_55
   Google Scholar
• Rezaia-Moghaddam, K., Karami, E., & Gibson, J. (2005). Conceptualizing sustainable agriculture Iran as an illustrative case. Journal of Sustainable Agriculture, 27, 25–56. doi:10.1300/J064v27n03_04
   Google Scholar
• Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representations by back-propagating errors. Nature, 323, 533–536.10.1038/323533a0
   Web of Science ®Google Scholar
• Sengur, A. (2008a). Wavelet transform and adaptive neuro-fuzzy inference system for color texture classification. Expert Sits Apple, 34, 2120–2128.10.1016/j.eswa.2007.02.032
   Web of Science ®Google Scholar
• Shakibai, T., & Koochekzadeh, S. (2009). Modeling and predicting agricultural energy consumption in Iran. American-Eurasian Journal of Agricultural and Environmental Science, 5, 308–312.
   Google Scholar
• Sroczynska, K., Claro, M., Kruk, A., Wojtal-Frankiewicz, A., Range, P., & Chícharo, L. (2017). Indicator macroinvertebrate species in a temporary Mediterranean river: Recognition of patterns in binary assemblage data with a Kohonen artificial neural network. Ecological Indicators, 73, 319–330. doi:10.1016/j.ecolind.2016.09.010
   Web of Science ®Google Scholar
• Taghavifar, H., & Mardani, A. (2013). Investigating the effect of velocity, inflation pressure, and vertical load on rolling resistance of a radial ply tire. Journal of Terramechanics, 50, 99–106.10.1016/j.jterra.2013.01.005
   Web of Science ®Google Scholar
• Taghavifar, H., & Mardani, A. (2015). Evaluating the effect of tire parameters on required drawbar pull energy model using adaptive neuro-fuzzy inference system. Energy, 85, 586–593, doi:10.1016/j.energy.2015.03.072
   Web of Science ®Google Scholar
• Taghavifar, H., Mardani, A., & Taghavifar, L. (2013). A hybridized artificial neural network and imperialist competitive algorithm optimization approach for prediction of soil compaction in soil bin facility. Measurement, 46, 2288–2299.10.1016/j.measurement.2013.04.077
   Web of Science ®Google Scholar
• Talatahari, S., Kavehand, A., & Sheikholeslami, R. (2012). Chaotic imperialist competitive algorithm for optimum design of truss structures. Structural and Multidisciplinary Optimization-incl, 46,355–367.10.1007/s00158-011-0754-4
   Web of Science ®Google Scholar
• Tiryaki, A. B. (2008). Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks, and regression trees. Engineering Geology, 99, 51–60.10.1016/j.enggeo.2008.02.003
   Web of Science ®Google Scholar
• Toro, J., Requena, I., & Zamorano, M. (2010). Environmental impact assessment in Colombia: Critical analysis and proposals for improvement. Environmental Impact Assessment Review, 30, 247–261. doi:10.1016/j.eiar.2009.09.001
   Web of Science ®Google Scholar
• Ubeyli, E. D. (2008). Adaptive neuro – Fuzzy inference system employing wavelet coefficients for detection of ophthalmic arterial disorders. Expert Systems with Applications, 34, 2201–2209.10.1016/j.eswa.2007.02.020
   Web of Science ®Google Scholar
• Ying, L., & Pan, M. (2008). Using adaptive network based fuzzy inference system to forecast regional electricity loads. Energy Conversion and Management, 49, 205–211.10.1016/j.enconman.2007.06.015
   Web of Science ®Google Scholar
• Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.10.1016/S0019-9958(65)90241-X
   Web of Science ®Google Scholar
• Zhang, X. (2012). The application of imperialist competitive algorithm based on Chaos theory in perceptron neural network. Physics Procedia, 25, 536–542.10.1016/j.phpro.2012.03.123
   Google Scholar