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This study aims to develop a novel model for accurate drought prediction by integrating signal decomposition and LSTM.

July 15, 2026WaterOpen Access

Accurate Multi-Scaler SPI Drought Prediction by Designing ORDWT-Based Signal Decomposition and Stacked LSTM

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Randomized trial demonstrates improved drought prediction accuracy in climatic regions, indicating enhanced water management strategies.

This study aims to develop a novel model for accurate drought prediction by integrating signal decomposition and LSTM.
Designed the ORDWT-SLSTM model for signal decomposition and drought prediction.
Evaluated model performance using SPI at multiple prediction horizons across three climatic regions.
Compared performance with benchmark models including GRU, LSTM, and BiLSTM using RMSE, MAE, and NSE.
ORDWT-SLSTM achieved RMSE of 0.215, MAE of 0.125, NSE of 0.986 in Darling Downs.
In Gatton, RMSE was 0.260, MAE was 0.147, NSE was 0.973.
For Mackay, RMSE was 0.225, MAE was 0.130, NSE was 0.980, demonstrating superior prediction performance.

Labban et al. (2026) studied this question.

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