D. K. Dwivedi J.H. Kelaiya G. R. Sharma


The onset, withdrawal and quantity of rainfall greatly influence the agricultural yield, economy, water resources, power generation and ecosystem. Time series modelling has been extensively used in stochastic hydrology for predicting various hydrological processes. The principles of stochastic processes have been increasingly and successfully applied in the past three decades to model many of the hydrological processes which are stochastic in nature. Time lagged models extract maximum possible information from the available record for forecasting. Artificial neural network has been found to be effective in modelling hydrological processes which are stochastic in nature. The ARIMA model was used to simulate and forecast rainfall using its linear approach and the performance of the model was compared with ANN. The computational approach of ANN is inspired from nervous system of living beings and the neurons possess the parallel distribution processing nature. ANN has proven to be a reliable tool for modelling compared to conventional methods like ARIMA and therefore ANN has been used in this study to estimate rainfall. In this study, rainfall estimation of Junagadh has been attempted using monthly rainfall training data of 32 years (1980-2011) and testing data of 5 years (2012-2016). A number of ANN model structures were tested, and the appropriate ANN model was selected based on its performance measures like root mean square error and correlation coefficient. The correlation coefficient Seasonal ARIMA (1,0,0)(3,1,1)12 and ANN back-propagation model (5-12-1) on the testing data was found to be 0.75 and 0.79 respectively. Seasonal ARIMA (1,0,0)(3,1,1)12 and ANN back-propagation model (5-12-1) were used for forecasting rainfall of 5 years (2017-2021).


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Artificial Neural Network, Autoregressive Integrated Moving Average, Forecasting, Rainfall, Time series modelling

Abdul-Aziz, A. R., Anokye, M., Kwame, A., Munyakazi, L. and Nsowah-Nuamah, N. N. N. (2013). Modeling and forecasting rainfall pattern in Ghana as a seasonal ARIMA process: the case of Ashanti region. International Journal of Humanities and Social Science, 3(3): 224-233
Bari, S. H., Rahman, M. T., Hussain, M. M. and Ray, S. (2015). Forecasting monthly precipitation in Sylhet city using ARIMA model. Civil and Environmental Research, 7(1): 69-77
Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control, Holden-day Publication, San Francisco.
Chattopadhyay, S. and Chattopadhyay, G. (2010). Univariate modelling of summer-monsoon rainfall time series: comparison between ARIMA and ARNN. Comptes Rendus Geoscience, 342(2):100-107
Kaushik, I. and Singh, S. M. (2008). Seasonal ARIMA model for forecasting of monthly rainfall and temperature. Journal of Environmental Research and Development, 3(2)
Nayak, D. R., Mahapatra, A. and Mishra, P. (2013). A survey on rainfall prediction using artificial neural network. International Journal of Computer Applications, 72(16)
Farajzadeh, J., Fard, A. F., and Lotfi, S. (2014). Modeling of monthly rainfall and runoff of Urmia lake basin using “feed-forward neural network” and “time series analysis” model. Water Resources and Industry, 7(1):38-48
French MN, Krajewski WF, Cuykendal RR (1992). Rainfall Forecastingin Space and Time Using a Neural Network. Journal of Hydrology, 137: 1–37.
Nayak, D. R., Mahapatra, A. and Mishra, P. (2013). A survey on rainfall prediction using artificial neural network. International Journal of Computer Applications, 72(16). DOI: 10.5120/12580-9217
Rumelhart, D. E., and McClelland, J. L.(1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition, London, UK, The MIT Press.
Somvanshi, V. K., Pandey, O. P. Agarwal, P. K. Kalanker, N. V. Prakesh, M. R., and Chand, R. (2006). Modeling and prediction of rainfall using artificial neural network and ARIMA techniques. Journal of Indian Geophysical Union, 10(2) : 141-151.
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Dwivedi, D. K., Kelaiya, J., & Sharma, G. R. (2019). Forecasting monthly rainfall using autoregressive integrated moving average model (ARIMA) and artificial neural network (ANN) model: A case study of Junagadh, Gujarat, India. Journal of Applied and Natural Science, 11(1), 35-41. https://doi.org/10.31018/jans.v11i1.1951
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