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

Ali Ghozat, Ahmad Sharafati*, Seyed Babak Haji Seyed Asadollah, Davide Motta

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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.
Original languageEnglish
Article number107946
Number of pages17
JournalComputers and Electronics in Agriculture
Early online date8 Jun 2023
Publication statusPublished - 1 Aug 2023

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