Vol. 7, Issue 1 (2019)
Forecast future rainfall & temperature for the study area using seasonal auto-regressive integrated moving averages (SARIMA) model
Author(s): Anosh Graham, Jyotish Kumar Sahu, Yogeshwar Kumar Sahu and Avinash Yadu
Abstract: Meteorological data is required to evaluate the long term effect of proposed man made hydrological changes. Such evaluations are often hydrologic processes which use weather data as input. The meteorological variables required for most of hydrologic models includes precipitation it is essential to have a sufficient long historic data to achieve an operating mathematical rule. Records which consist of short term data are not suitable for proper planning and arrangement processes and also available of a location of interest. in such circumstances, it become necessary to deterministic as well as stochastic component, but stochastic time series models, such as autoregressive (AR), moving average(MA) autoregressive and moving average (ARMA) are widely used to developed and generate the rainfall and temperature. They apply Seasonal ARIMA models that counted for 92% of the total variability in the monthly means of air temperature. Their forecasted values showed good agreement with the actual observed values of temperature. They concluded that for highly variable time series, ARIMA models yield better forecasts than the simple models which are only based on means of previous observation.
Fig. 1: Box Jenkins arima model
Pages: 894-897 | 384 Views 74 Downloads
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How to cite this article:
Anosh Graham, Jyotish Kumar Sahu, Yogeshwar Kumar Sahu, Avinash Yadu. Forecast future rainfall & temperature for the study area using seasonal auto-regressive integrated moving averages (SARIMA) model. Int J Chem Stud 2019;7(1):894-897.
