A novel intelligent approach for predicting meteorological drought based on satellite-based precipitation product: Application of an EMD-DFA-DBN hybrid model

Abstract

This study uses the long-term Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) dataset with 0.05° spatial resolution to calculate the Standardized Precipitation Index (SPI) signal. The combination of Empirical Mode Decomposition (EMD), Detrended Fluctuation Analysis (DFA) and Deep Belief Network (DBN) as a hybrid model is applied to predict the SPI in the short and long term, i.e. lead times of one, six and twelve months. The performance of the hybrid EMD-DFA-DBN prediction model is evaluated against the hybrid EMD-DFA-MLP model and the stand-alone MLP (Multi-Layer Perceptron) and DBN models. The SPI signal, reconstructed using the EMD-DFA pre-processing method, where the EMD method decomposes the SPI signal into Intrinsic Mode Functions (IMFs) and the DFA method separates high-frequency noisy IMFs from low-frequency useful ones (SNR ≈ 6.49, PSNR ≈ 15.85, CC ≈ 0.88), has lower noise than the original signal; this allows for more accurate predictions of the SPI signal: the DBN-based prediction, with the algorithm trained using the reconstructed SPI signal (CC ≈ 0.94–0.98, RMSE ≈ 0.23–0.38) is superior to that obtained with the algorithm trained using the original SPI signal (CC ≈ 0.87–0.90, RMSE ≈ 0.47–0.51), as well as the MLP-based prediction with training on the reconstructed SPI signal (CC ≈ 0.92–0.95, RMSE ≈ 0.24–0.42) or the original SPI signal (CC ≈ 0.87–0.88, RMSE ≈ 0.48–0.51). It is found that selecting an appropriate stopping criterion for the sifting process is crucial to correctly decompose and reconstruct SPI with the EMD-DFA method. Using the EMD-DFA-DBN hybrid model reduces the noise but preserves useful information in the SPI signal and allows for accurate prediction of drought through deep learning.