Forecasting monthly rainfall using autoregressive integrated moving average model (ARIMA) and artificial neural network (ANN) model: A case study of Junagadh, Gujarat, India

• Published in: Journal of applied and natural science Vol. 11; no. 1; pp. 35 - 41
• Main Authors: Dwivedi, D. K., Kelaiya, J.H., Sharma, G. R.
• Format: Journal Article
• Language: English
• Published: Haridwar Applied and Natural Science Foundation 2019
• Subjects: Artificial neural networks; Autoregressive models; Back propagation; Back propagation networks; Correlation coefficient; Correlation coefficients; Error analysis; Forecasting; Hydrology; Information processing; Model testing; Nervous system; Neural networks; Rainfall; Stochastic hydrology; Stochastic processes; Stochasticity; Water resources; Gujarat India; India

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 proce...