Abstract
Forecasting yield is a challenging task in all agricultural crops. So, it is imperative to develop a machine learning hybrid model with available data for yield forecasting. The main objective of this research is to develop a novel hybrid model for forecasting the sugarcane yield on non-linear time series data. Recurrent neural network typically holds a long memory that allows sufficient forecasts with a fewer number of parameters. The weights and thresholds of the recurrent neural network are optimized by whale optimization algorithm to improve the efficiency of the neural network, and thus obtaining accurate results. Consecutively increasing the performance in forecasting volatility of time series is a challenging task. BPNN-GARCH, RNN-GARCH, and novel WOARNN-GARCH referred to as hybrid models; fitted with precipitation as well as sugarcane yield data and a novel method was found more appropriate for forecasting the volatility of rainfall as well as sugarcane yield for medium-term which facilitated in observing the future incidences for the next few years. Combining a statistical model like generalized autoregressive conditional heteroskedasticity with recurrent neural network refined by whale optimization algorithm grants a novelty of predicting the yield with volatility in time series analysis.
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References
Aljarah I, Faris H, Mirjalili S (2018) Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Comput 22(1):1–15
Arabeyyat O, Shatnawi N, Matouq M (2018) Nonlinear multivariate rainfall prediction in jordan using NARX-ANN model with GIS techniques. Jordan J Civ Eng 12(3):359–368
Arneric J, Poklepovic T, Aljinovic Z (2014) GARCH based artificial neural networks in forecasting conditional variance of stock returns. Croat Oper Res Rev 5(2):329–343
Baboota R, Kaur H (2019) Predictive analysis and modelling football results using machine learning approach for English Premier League. Int J Forecast 35(2):741–755
Borovkova S, Tsiamas I (2018) An ensemble of LSTM neural networks for high-frequency stock market classification. J Forecast 38(6):600–619
Chen MY (2013) Time series analysis: conditional volatility models. Research report, Department of finance, National chung hsing university, pp 1–47
Dabral PP, Murry MZ (2017) Modelling and forecasting of rainfall time series using SARIMA. Environ Process 4(2):399–419
Diao Z, Zhang D, Wang X, Xie K, He S, Lu X, Li Y (2018) A hybrid model for short-term traffic volume prediction in massive transportation systems. IEEE Trans Intell Transp Syst 99:1–12
Do Nascimento Camelo H, Lucio PS, Junior JBVL, de Carvalho PCM (2018) A hybrid model based on time series models and neural network for forecasting wind speed in the Brazilian northeast region. Sustain Energy Technol Assess 28:65–72
Gavali KB, Bewoor AK, Barik D (2018) Effective utilization of job shop scheduling in auto industries with the aid of social spider optimization. J Green Eng 8(4):475–496
Gogas P, Papadimitriou T, Agrapetidou A (2018) Forecasting bank failures and stress testing: a machine learning approach. Int J Forecast 34(3):440–455
Gupta BB, Agrawal DP, Yamaguchi S (2019) Deep learning models for human centered computing in fog and mobile edge networks. J Amb Intell Humaniz Comput 10:2907–2911
Hao H, Zhang Q, Wang Z, Zhang J (2018) Forecasting the number of end-of-life vehicles using a hybrid model based on grey model and artificial neural network. J Clean Prod 202:684–696
Huang H, Ho CC (2012) Back-propagation neural network combined with a particle swarm optimization algorithm for travel package demand forecasting. Int J Digit Content Technol Appl 6(12):194–202
He JP, Cai L, Cheng P, Fan JL (2015) Optimal investment for retail company in electricity market. IEE Trans Ind Informat 11(5):1210–1219
Javari M (2017) Assessment of dynamic linear and non-linear models on rainfall variations predicting of Iran. Agric Eng Int CIGR J 19(2):224–240
Kim HY, Won CH (2018) Forecasting the volatility of stock price index: a hybrid model integrating LSTM with multiple GARCH-type models. Expert Syst Appl 103:25–37
Koima JK, Mwita PN, Nassiuma DK (2015) Volatility estimation of stock prices using GARCH method. Eur J Bus Manag 7(19):108–113
Kristjanpoller W, Minutolo MC (2018) A hybrid volatility forecasting framework integrating GARCH, artificial neural network, technical analysis and principal components analysis. Expert Syst Appl 109:1–11
Li G, Ma X, Yang H (2018) A hybrid model for forecasting sunspots time series based on variational mode decomposition and backpropagation neural network improved by firefly algorithm. Comput Intell Neurosci. https://doi.org/10.1155/2018/3713410
Liu Z, Guo J, Cao J, Wei Y, Huang W (2018) A hybrid short-term traffic flow forecasting method based on neural networks combined with k-nearest neighbor. Promet Traffic Transp 30(4):445–456
Luo R, Zhang W, Xu X, Wang J (2017) A neural stochastic volatility model, arXiv preprint arXiv:1712.00504
Machekposhti KH, Sedghi H, Telvari A, Babazadeh H (2017) Modelling rainfall in karkheh dam reservoir of iran using time series analysis (stochastic ARIMA models). Leban Sci J 18(2):204–218
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
Murat M, Malinowska I, Gos M, Krzyszczak J (2018) Forecasting daily meteorological time series using ARIMA and regression models. Int Agrophys 32(2):253–264
Patel MGC, Shettigar AK, Krishna P, Parappagoudar MB (2017) Back propagation genetic and recurrent neural network applications in modelling and analysis of squeeze casting process. Appl Soft Comput 59:418–437
Patrício DI, Rieder R (2018) Computer vision and artificial intelligence in precision agriculture for grain crops: a systematic review. Comput Electron Agric 153:69–81
Radha P, Meena Preethi B (2019) Machine learning approaches for disease prediction from radiology and pathology reports. J Green Eng 9(2):149–166
Rampone S, Valente A (2019) Assessment of desertification vulnerability using soft computing methods. J Amb Intell Humaniz Comput 10(2):701–707
Too EC, Yujian L, Njuki S, Yingchun L (2018) A comparative study of fine-tuning deep learning models for plant disease identification. Comput Electron Agric 161(2019):272–279
Zhang S, Chang T, Lin D (2018) A preliminary study on a hybrid wavelet neural network model for forecasting monthly rainfall. Eurasia J Math Sci Technol Educ 14(5):1747–1757
Acknowledgements
The first author would like to thank the management of Kalasalingam Academy of Research and Education for providing fellowship. Authors also grant gratitude towards the Director, ICAR-Sugarcane Breeding Institute, Coimbatore for providing the necessary data to carry out the research work.
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Murali, P., Revathy, R., Balamurali, S. et al. Integration of RNN with GARCH refined by whale optimization algorithm for yield forecasting: a hybrid machine learning approach. J Ambient Intell Human Comput (2020). https://doi.org/10.1007/s12652-020-01922-2
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DOI: https://doi.org/10.1007/s12652-020-01922-2