Wavelet based hybrid ANN-ARIMA models for meteorological drought forecasting

• Published in: Journal of hydrology (Amsterdam) Vol. 590; p. 125380
• Main Authors: Khan, Md. Munir Hayet, Muhammad, Nur Shazwani, El-Shafie, Ahmed
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2020
• Subjects: ANN-ARIMA; atmospheric precipitation; Discrete wavelet; drought; Drought forecasting; hydrologic data; Malaysia; meteorological data; neural networks; prediction; risk management; SIAP and SPI; time series analysis; watersheds; wavelet; Malaysia; SIAP and SPI; Discrete wavelet; ANN-ARIMA; Drought forecasting
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/j.jhydrol.2020.125380

•Merge the advantages of the Wavelet, ARIMA and ANN for drought forecasting.•Propose new model structure using the time series of SIAP and SPI.•High and low frequency sub-series were passed through ANN and ARIMA respectively.•Comparison between parametric SPI drought index with non-parametric SIAP.•Possibility of using this model in flood forecasting study. Drought prediction is an important subject, particularly in drought-hydrology, and has a key role in risk management, drought readiness and alleviation. Hydrological time series data consists of nonlinear features and various time scales. With this view in mind, this study has combined the strengths of the Wavelet transformation, Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) to test a new method of a hybrid model for their ability to accurately predict future droughts. A 30-year rainfall data from the year 1986 to 2016 for Malaysia’s Langat River Basin was analyzed. Meteorological drought indices (DI) such as the Standardized Precipitation Index (SPI) and the Standard Index of Annual Precipitation (SIAP) were used to compute historical drought events. At first, each of these computed drought time series went through a process of decomposition to be divided as low frequency and high-frequency sub-series by discrete wavelet transform (DWT). Secondly, the high and low-frequency sub-series were passed through the predictive model of ANN and ARIMA techniques, respectively. Lastly, the predicted sub-series were used to reconstruct and develop a final drought prediction model. It was found that the Wavelet-ARIMA-ANN (which named as W-2A) model outperformed the single ANN and wavelet-ANN predictive models. The ANN model developed by SPI achieved an overall correlation co-efficient R-value of 0.423, but the wavelet-based ANN model decreased in the R-value to 0.415. Finally, two different models, which were established using drought indices SPI and SIAP, and discrete wavelet transformation-based hybrid ANN-ARIMA (W-2A), have achieved improved R values of 0.914 and 0.934 respectively.