Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network

References

• Adam, M., Ewert, F., Leffelaar, P. A., Corbeels, M., Van Keulen, H., & Wery, J. (2010). CROSPAL, software that uses agronomic expert knowledge to assist modules selection for crop growth simulation. Environmental Modelling & Software, 25(8), 946–955. %@ 1364–8152. doi: 10.1016/j.envsoft.2010.02.007
   Web of Science ®Google Scholar
• Ahmad, M. (1992). Supervised learning using the Cauchy energy function. Proc. Int. Conf. on Fuzzy Logic and Neural Network.
   Google Scholar
• Ameli, F., Hemmati-Sarapardeh, A., Schaffie, M., Husein, M. M., & Shamshirband, S. (2018). Modeling interfacial tension in N2/n-alkane systems using corresponding state theory: Application to gas injection processes. Fuel, 222, 779–791. doi: 10.1016/j.fuel.2018.02.067
   Web of Science ®Google Scholar
• Asadi, S., Shahrabi, J., Abbaszadeh, P., & Tabanmehr, S. (2013). A new hybrid artificial neural networks for rainfall–runoff process modeling. Neurocomputing, 121, 470–480. %@ 0925-2312. doi: 10.1016/j.neucom.2013.05.023
   Web of Science ®Google Scholar
• Ayatollahi, S., Hemmati-Sarapardeh, A., Roham, M., & Hajirezaie, S. (2016). A rigorous approach for determining interfacial tension and minimum miscibility pressure in paraffin-CO2 systems: Application to gas injection processes. Journal of the Taiwan Institute of Chemical Engineers, 63, 107–115. doi: 10.1016/j.jtice.2016.02.013
   Web of Science ®Google Scholar
• Bahrani, M., Shomeili, M., Zande-Parsa, S., & Kamgar-Haghighi, A. (2010). Sugarcane responses to irrigation and nitrogen in subtropical Iran. Iran Agricultural Research, 27–21. .2), pp. 17–26.
   Google Scholar
• Barzegar, A., Asoodar, M., & Ansari, M. (2000). Effectiveness of sugarcane residue incorporation at different water contents and the proctor compaction loads in reducing soil compactibility. Soil and Tillage Research, 57(3), 167–172. doi: 10.1016/S0167-1987(00)00158-6
   Web of Science ®Google Scholar
• Chau, K.-W. (2017). Use of meta-heuristic techniques in rainfall-runoff modelling. Multidisciplinary Digital Publishing Institute.
   Google Scholar
• Chen, X., Chau, K., & Busari, A. (2015). A comparative study of population-based optimization algorithms for downstream river flow forecasting by a hybrid neural network model. Engineering Applications of Artificial Intelligence, 46, 258–268. doi: 10.1016/j.engappai.2015.09.010
   Web of Science ®Google Scholar
• Dashtegol, A., Kashkooli, H., Naseri, A., & Nasab, S. (2009). Effects of every-other furrow irrigation on water use efficiency and sugarcane characteristics in southern Ahvaz sugarcane fields. Journal of Science and Technology of Agriculture and Natural Resources, 13(49 (B)), 45–58.
   Google Scholar
• FAO. (2015a). Crop production. F. a. A. O. o. t. U. Nations.
   Google Scholar
• FAO. (2015b). Sugarcane production. Author.
   Google Scholar
• Fortin, J. G., Anctil, F., Parent, L-É, & Bolinder, M. A. (2010). A neural network experiment on the site-specific simulation of potato tuber growth in Eastern Canada. Computers and Electronics in Agriculture, 73(2), 126–132. %@ 0168-1699. doi: 10.1016/j.compag.2010.05.011
   Web of Science ®Google Scholar
• Gago, J., Martínez-Núñez, L., Landín, M., & Gallego, P. P. (2010). Artificial neural networks as an alternative to the traditional statistical methodology in plant research. Journal of Plant Physiology, 167(1), 23–27. %@ 0176-1617. doi: 10.1016/j.jplph.2009.07.007
   PubMed Web of Science ®Google Scholar
• Gholami, V., Chau, K., Fadaee, F., Torkaman, J., & Ghaffari, A. (2015). Modeling of groundwater level fluctuations using dendrochronology in alluvial aquifers. Journal of Hydrology, 529, 1060–1069. doi: 10.1016/j.jhydrol.2015.09.028
   Web of Science ®Google Scholar
• Ghouti, L., Sheltami, T. R., & Alutaibi, K. S. (2013). Mobility prediction in mobile ad hoc networks using extreme learning machines. Procedia Computer Science, 19, 305–312. doi: 10.1016/j.procs.2013.06.043
   Google Scholar
• Gori, M., & Tesi, A. (1992). On the problem of local minima in backpropagation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(1), 76–86. doi: 10.1109/34.107014
   Web of Science ®Google Scholar
• Hamdi, H., Baniabbasi, N., Almani, M. P., & Babagoli, S. (2005). Advances in TAE Sugarcane Breeding Program in Iran. Proc. ISSCT.
   Google Scholar
• Hemmati-Sarapardeh, A., & Mohagheghian, E. (2017). Modeling interfacial tension and minimum miscibility pressure in paraffin-nitrogen systems: Application to gas injection processes. Fuel, 205, 80–89. doi: 10.1016/j.fuel.2017.05.035
   Web of Science ®Google Scholar
• Huang, G.-B., Chen, L., & Siew, C. K. (2006). Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks, 17(4), 879–892. doi: 10.1109/TNN.2006.875977
   PubMed Web of Science ®Google Scholar
• Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2004). Extreme learning machine: a new learning scheme of feedforward neural networks. Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on.
   Google Scholar
• Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006a). Extreme learning machine: Theory and applications. Neurocomputing, 70(1), 489–501. doi: 10.1016/j.neucom.2005.12.126
   Web of Science ®Google Scholar
• Huang, G.-B., Zhu, Q.-Y., & Siew, C. K. (2006b). Real-time learning capability of neural networks. IEEE Transactions on Neural Networks, 17(4), 863–878. doi: 10.1109/TNN.2006.875974
   PubMed Web of Science ®Google Scholar
• Jeganathan, C., Roy, P. S., & Jha, M. N. (2010). Markov model for predicting the land cover changes in Shimla district. Indian Forester, 136(5), 667–686. 0019-4816.
   Google Scholar
• Karimi, M., RajabiPour, A., Tabatabaeefar, A., & Borghei, A. (2008). Energy analysis of sugarcane production in plant farms a case study in Debel Khazai agro-industry in Iran. American-Eurasian Journal of Agricultural and Environmental Science, 4, 165–171.
   Google Scholar
• Kaul, M., Hill, R. L., & Walthall, C. (2005). Artificial neural networks for corn and soybean yield prediction. Agricultural Systems, 85(1), 1–18. %@ 0308-0521X. doi: 10.1016/j.agsy.2004.07.009
   Web of Science ®Google Scholar
• Liang, N.-Y., Huang, G.-B., Saratchandran, P., & Sundararajan, N. (2006). A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Transactions on Neural Networks, 17(6), 1411–1423. doi: 10.1109/TNN.2006.880583
   PubMed Web of Science ®Google Scholar
• Lotfinejad, M. M., Hafezi, R., Khanali, M., Hosseini, S. S., Mehrpooya, M., & Shamshirband, S. (2018). A comparative assessment of predicting daily solar radiation using bat neural network (BNN), generalized regression neural network (GRNN), and neuro-fuzzy (NF) system: A case study. Energies, 11(5), 1188. doi: 10.3390/en11051188
   Web of Science ®Google Scholar
• Moayedi, H., & Hayati, S. (2018). Artificial intelligence design charts for predicting friction capacity of driven pile in clay. Neural Computing and Applications, 29(1), 1–17. doi: 10.1007/s00521-017-3243-x
   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. doi: 10.1080/19942060.2018.1482476
   Web of Science ®Google Scholar
• Mosavi, A., Bathla, Y., & Varkonyi-Koczy, A. (2017). Predicting the future using web knowledge: State of the art survey. International Conference on Global Research and Education.
   Google Scholar
• Mosavi, A., & Rabczuk, T. (2017a). Learning and intelligent optimization for computational materials design innovation.
   Google Scholar
• Mosavi, A., & Rabczuk, T. (2017b). Learning and intelligent optimization for material design innovation. Learning and Intelligent Optimization: Springer.
   Google Scholar
• Mosavi, A., Rabczuk, T., & Varkonyi-Koczy, A. R. (2017). Reviewing the novel machine learning tools for materials design. Recent Advances in Technology Research and Education: Springer Nature.
   Google Scholar
• Najafi, B., Faizollahzadeh Ardabili, S., Mosavi, A., Shamshirband, S., & Rabczuk, T. (2018). An intelligent artificial neural network-response surface methodology method for accessing the optimum biodiesel and diesel fuel blending conditions in a diesel engine from the viewpoint of exergy and energy analysis. Energies, 11(4), 860. doi:10.3390/en11040860
   Web of Science ®Google Scholar
• Nazari, M., & Shamshirband, S. (2018). The particle filter-based back propagation neural network for evapotranspiration estimation. ISH Journal of Hydraulic Engineering. doi: 10.1080/09715010.2018.1481462
   Google Scholar
• Nian, R., He, B., Zheng, B., Van Heeswijk, M., Yu, Q., Miche, Y., & Lendasse, A. (2014). Extreme learning machine towards dynamic model hypothesis in fish ethology research. Neurocomputing, 128, 273–284. doi: 10.1016/j.neucom.2013.03.054
   Web of Science ®Google Scholar
• Olyaie, E., Banejad, H., Chau, K.-W., & Melesse, A. M. (2015). A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: A case study in United States. Environmental Monitoring and Assessment, 187(4), 189. doi: 10.1007/s10661-015-4381-1
   PubMed Web of Science ®Google Scholar
• Osama, K., Mishra, B. N., & Somvanshi, P. (2015). Machine Learning Techniques in Plant Biology. PlantOmics: The Omics of Plant Science (pp. 731-754%@ 8132221710): Springer.
   Google Scholar
• Rugege, D. (2002). Regional analysis of maize-based land use systems for early warning applications.
   Google Scholar
• Samet, S., & Miri, A. (2012). Privacy-preserving back-propagation and extreme learning machine algorithms. Data & Knowledge Engineering, 79, 40–61. doi: 10.1016/j.datak.2012.06.001
   Web of Science ®Google Scholar
• Sehgal, V., Sahay, R. R., & Chatterjee, C. (2014). Effect of utilization of discrete wavelet components on flood forecasting performance of wavelet based ANFIS models. Water Resources Management, 28(6), 1733–1749. %@ 0920-4741. doi: 10.1007/s11269-014-0584-4
   Web of Science ®Google Scholar
• Singh, R., & Balasundaram, S. (2007). Application of extreme learning machine method for time series analysis. International Journal of Intelligent Technology, 2(4), 256–262.
   Google Scholar
• Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. Advances in neural information processing systems.
   Google Scholar
• Taherei Gazvinei, P. (2007). Assessment the Changes of the Irrigation & Drainage Water Levels on the Sugarcane Growth and Production (PhD. Research and Science Azad University, Ahwaz.
   Google Scholar
• Taormina, R., & Chau, K.-W. (2015). Data-driven input variable selection for rainfall–runoff modeling using binary-coded particle swarm optimization and extreme learning machines. Journal of Hydrology, 529, 1617–1632. doi: 10.1016/j.jhydrol.2015.08.022
   Web of Science ®Google Scholar
• Tiwari, M., & Chatterjee, C. (2011). A new wavelet-bootstrap-ANN hybrid model for daily discharge forecasting. Journal of Hydroinformatics, 13(3), 500–519. doi: 10.2166/hydro.2010.142
   Web of Science ®Google Scholar
• Torabi, M., Hashemi, S., Saybani, M. R., Shamshirband, S., & Mosavi, A. (2018). A hybrid clustering and classification technique for forecasting short-term energy consumption. Environmental Progress & Sustainable Energy. doi: 10.1002/ep.12934
   Web of Science ®Google Scholar
• Van Ooyen, A., & Nienhuis, B. (1992). Improving the convergence of the back-propagation algorithm. Neural Networks, 5(3), 465–471. doi: 10.1016/0893-6080(92)90008-7
   Web of Science ®Google Scholar
• Wang, W.-c., Chau, K.-w., Xu, D.-m., Qiu, L., & Liu, C.-c. (2017). The annual maximum flood peak discharge forecasting using Hermite projection pursuit regression with SSO and LS method. Water Resources Management, 31(1), 461–477. doi: 10.1007/s11269-016-1538-9
   Web of Science ®Google Scholar
• Wang, X., & Han, M. (2014). Online sequential extreme learning machine with kernels for nonstationary time series prediction. Neurocomputing, 145, 90–97. doi: 10.1016/j.neucom.2014.05.068
   Web of Science ®Google Scholar
• Wang, D. D., Wang, R., & Yan, H. (2014). Fast prediction of protein–protein interaction sites based on extreme learning machines. Neurocomputing, 128, 258–266. doi: 10.1016/j.neucom.2012.12.062
   Web of Science ®Google Scholar
• Willocquet, L., Elazegui, F. A., Castilla, N., Fernandez, L., Fischer, K. S., Peng, S., … Zhu, D. (2004). Research priorities for rice pest management in tropical Asia: A simulation analysis of yield losses and management efficiencies. Phytopathology, 94(7), 672–682. %@ 0031-0949X. doi: 10.1094/PHYTO.2004.94.7.672
   PubMed Web of Science ®Google Scholar
• Wong, P. K., Wong, K. I., Vong, C. M., & Cheung, C. S. (2015). Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search. Renewable Energy, 74, 640–647. doi: 10.1016/j.renene.2014.08.075
   Web of Science ®Google Scholar
• Yu, Q., Miche, Y., Séverin, E., & Lendasse, A. (2014). Bankruptcy prediction using extreme learning machine and financial expertise. Neurocomputing, 128, 296–302. doi: 10.1016/j.neucom.2013.01.063
   Web of Science ®Google Scholar
• Zhang, J.-R., Zhang, J., Lok, T.-M., & Lyu, M. R. (2007). A hybrid particle swarm optimization–back-propagation algorithm for feedforward neural network training. Applied Mathematics and Computation, 185(2), 1026–1037. doi: 10.1016/j.amc.2006.07.025
   Web of Science ®Google Scholar
• Zhong, H., Miao, C., Shen, Z., & Feng, Y. (2014). Comparing the learning effectiveness of BP, ELM, I-ELM, and SVM for corporate credit ratings. Neurocomputing, 128, 285–295. doi: 10.1016/j.neucom.2013.02.054
   Web of Science ®Google Scholar