Abstract
Abstract Drought prediction plays an important guiding role in drought risk management. The standardized precipitation index (SPI) is a popular meteorological drought indicator to measure the degree of drought. The SPI time series is non-stationary, whereas the conventional artificial neural network (ANN) model has limitations to predict non-stationary time series. To overcome this limitation, it is essential to investigate input data preprocessing to improve the ANN model. In this paper, a hybrid model coupled with singular spectrum analysis (SSA) and backpropagation ANN is proposed (SSA-BP-ANN). The advantage of this model is that the SSA of finite-length SPI sequences does not require the adoption of boundary extensions to suppress boundary effects, while the most predictable components of the SPI can be efficiently extracted and incorporated into the model. The proposed SSA-BP-ANN model is tested in case studies at three meteorological stations in Northern Shannxi Province, China. The results show that the SSA-BP-ANN model can produce more accurate predictions than the BP-ANN model. In addition, the performance improvement of SSA on the BP-ANN model is slightly better than wavelet decomposition and empirical mode decomposition. This new hybrid prediction model has great potential for promoting drought early warning in arid regions.
Highlights
From a temporal perspective, humans have been struggling with drought for the last thousand years (Dai 2011)
Belayneh et al (2014) used wavelet transform to do data preprocessing on the model input, to build wavelet analysis (WA)-artificial neural network (ANN) and WA-SVR models for long-term standardized precipitation index (SPI) prediction, and the results show that the WA-ANN model has the best performance
A prediction model coupled with singular spectrum analysis (SSA) and BP-ANN was established and applied to the prediction of SPI-12 1 month ahead at three stations in northern Shaanxi, China
Summary
Humans have been struggling with drought for the last thousand years (Dai 2011). Drought is one of the most common natural disasters globally, characterized by high frequency, broad impact, long duration and high economic losses (Zhang et al 2015a, 2015b). The research on drought has attracted much attention world widely (Haile et al 2019; Balti et al 2020; Kaur & Sood 2020). Drought generally can be divided into four categories: meteorological, agricultural, hydrological and socio-economic droughts (Kimwatu et al 2021; Kolachian & Saghafian 2021; Wu et al 2021; Zhang et al 2021). Many scholars have focused their attention on monitoring and preventing meteorological droughts.
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