Abstract
Rainfall prediction is a challenging task due to its dependency on many natural phenomenon. Some authors used Hurst exponent as a predictability indicator to ensure predictability of the time series before prediction. In this paper, a detailed analysis has been done to ascertain whether a definite relation exists between a strong Hurst exponent and predictability. The one-lead monthly rainfall prediction has been done for 19 rain gauge station of the Yarra river basin in Victoria, Australia using Artificial Neural Network. The prediction error in terms of normalized Root Mean Squared Error has been compared with Hurst exponent. The study establishes the truth of the hypothesis for only 6 stations out of 19 stations, and thus recommends further investigation to prove the hypothesis. This concept is relevant for any time series which need to be used for real time process control.
Highlights
Due to rapid growth of population, urbanization and industrialization, the demand for water has increased tremendously
This study investigates the Hurst exponent of 19 stations in the Yarra River catchment in Victoria, Australia
Many methods are available to find the Hurst exponent, of which the rescaled range (R/S) analysis method is commonly adopted by many researchers [18, 19, 20].To predict the rainfall, Artificial Neural Network (ANN) is used in this study
Summary
Due to rapid growth of population, urbanization and industrialization, the demand for water has increased tremendously. Pre-processing of inputs has been commonly used to improve rainfall prediction using ANN [16]. Some authors used Hurst exponent as a measure of predictability, but the actual predictability is not considered in the scope of their reported works [17, 18, 19, 20]. The work reported by Khalili et al.[5] is an attempt towards demonstrating the relationship between Hurst exponent and predictability of a monthly rainfall time series of 50 years. The relationship between Hurst exponent and predictability of rainfall time series has not received sufficient attention, it is necessary to investigate in more detail the validity of this hypothesis.
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