出版社：The International Institute for Science, Technology and Education (IISTE)
摘要：In this study a seasonal ARIMA model was built using Box and Jenkins method to forecast long term rainfall in Sylhet. For this purpose rainfall data from 1980 to 2010 of Sylhet station were used to build and check the model. Rainfall data from 1980 to 2006 were used to develop the model while data from 2007 to 2010 were used to verify the prediction precision. Four basic chronological steps namely: identification, estimation, diagnostic checking, and forecasting were fitted out in developing the model. Validity of the model was tested using standard graphical explanation of residuals given by Box and Jenkins. As a second step of validation, forecasted values of monthly rainfall were checked using actual data series. After completion of necessary checking and forecast observation, the ARIMA(0, 0, 1) (1,1, 1) 12 was found to be the most effective to predict future precipitation with a 95% confidence interval. It is expected that this long term prediction will help decision makers in efficient scheduling of flood prediction, urban planning, rainwater harvesting and crop management.
其他摘要：In this study a seasonal ARIMA model was built using Box and Jenkins method to forecast long term rainfall in Sylhet. For this purpose rainfall data from 1980 to 2010 of Sylhet station were used to build and check the model. Rainfall data from 1980 to 2006 were used to develop the model while data from 2007 to 2010 were used to verify the prediction precision. Four basic chronological steps namely: identification, estimation, diagnostic checking, and forecasting were fitted out in developing the model. Validity of the model was tested using standard graphical explanation of residuals given by Box and Jenkins. As a second step of validation, forecasted values of monthly rainfall were checked using actual data series. After completion of necessary checking and forecast observation, the ARIMA(0, 0, 1) (1,1, 1) 12 was found to be the most effective to predict future precipitation with a 95% confidence interval. It is expected that this long term prediction will help decision makers in efficient scheduling of flood prediction, urban planning, rainwater harvesting and crop management. Keywords: Nonlinear time series analysis, ARIMA model, rainfall forecasting, Sylhet.
关键词：Nonlinear time series analysis; ARIMA model; rainfall forecasting; Sylhet.
