Abstract
Increased demand for engineering propositions to forecast rainfall events in an area or region has resulted in developing different rainfall prediction models. Interestingly, rainfall is a very complicated natural system that requires consideration of various attributes. However, regardless of the predictability performance, easy to use models have always been welcomed over the complex and ambiguous alternatives. This study presents the development of Auto–Regressive Integrated Moving Average models with exogenous input (ARIMAX) to forecast autumn rainfall in the South West Division (SWD) of Western Australia (WA). Climate drivers such as Indian Ocean Dipole (IOD) and El Nino Southern Oscillation (ENSO) were used as predictors. Eight rainfall stations with 100 years of continuous data from two coastal regions (south coast and north coast) were selected. In the south coast region, Albany (0,1,1) with exogenous input DMIOct–Nino3Nov, and Northampton (0,1,1) with exogenous input DMIJan–Nino3Nov were able to forecast autumn rainfall 4 months and 2 months in advance, respectively. Statistical performance of the ARIMAX model was compared with the multiple linear regression (MLR) model, where for calibration and validation periods, the ARIMAX model showed significantly higher correlations (0.60 and 0.80, respectively), compared to the MLR model (0.44 and 0.49, respectively). It was evident that the ARIMAX model can predict rainfall up to 4 months in advance, while the MLR has shown strict limitation of prediction up to 1 month in advance. For WA, the developed ARIMAX model can help to overcome the difficulty in seasonal rainfall prediction as well as its application can make an invaluable contribution to stakeholders’ economic preparedness plans.
Highlights
Long–term rainfall forecasting is one of the most challenging and demanding tasks
Statistical performance of the ARIMAX model was compared with the multiple linear regression (MLR) model, where for calibration and validation periods, the ARIMAX
From earlier correlation analysis, a combination of influential climate indices was used as exogenous variables (Predictors) in ARIMAX modeling
Summary
Long–term rainfall forecasting is one of the most challenging and demanding tasks. This is an important issue, which is directly related to the economy of a country. Two methods (statistical and dynamic modeling) have been used to forecast long–term rainfall [1]. A statistical method is less complex and requires less development time, but an uninterrupted and reliable data source is mandatory. The dynamic method is quite complex, requires more development time and expense [2,3]. The application of statistical methods to forecast rainfall has become very popular among the researchers due to its simplicity, cost–effectiveness, and easy to implement characteristics. The use of the physically–based empirical model for seasonal rainfall prediction has become popular since it overcomes the difficulties associated with conventional dynamic and statistical models [4,5]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
