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<p>This study investigates the integration of wavelet preprocessing with machine learning models to enhance wet-period rainfall prediction in the Northern Territory of Australia. Lagged large-scale climate indices, including the El Niño Southern Oscillation (ENSO), Dipole Mode Index (DMI), Madden-Julian Oscillation (MJO), and Interdecadal Pacific Oscillation (IPO), were employed as predictors for Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) models, both with and without wavelet transformation. Results indicate that hybrid models consistently outperformed their standalone counterparts, with the Wavelet-LSTM (W-LSTM) using Daubechies (db) wavelet emerging as the most effective configuration. Across three stations (Warruwi, Waterloo, and Avon Downs), the W-LSTM (db) achieved high correlations with observed rainfall (0.86–0.95) and substantially reduced RMSE, by up to 89.6% at Warruwi. It also demonstrated a strong ability to capture rainfall peaks and troughs, with correlations ranging from 0.76 to 0.91. Performance metrics further confirmed its superiority, with R² values of 0.80–0.89 and NSE values of 0.78–0.87, compared to the relatively lower performance of individual models (R² ranges 0.74–0.81). Although some challenges remain in accurately predicting extreme rainfall events, the overall findings present the advantages of combining wavelet preprocessing with machine learning. The W-LSTM (db) model provides a more robust and reliable approach to rainfall forecasting by improving the representation of variability and extremes. These results indicate the potential of hybrid wavelet-machine learning frameworks for enhancing predictive capability in regions influenced by high climate variability, with broader applicability to other tropical and monsoonal environments.</p> Graphical Abstract <p></p>

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Can Integrated Machine Learning Models Improve Rainfall Forecasting? A Case Study from the Australian Tropics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月18日

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  • Rashid Farooq,
  • Monzur Alam Imteaz,
  • Muhammad Atiq Ur Rehman Tariq,
  • Kamila Hlavčová,
  • Muhammad Waseem

摘要

This study investigates the integration of wavelet preprocessing with machine learning models to enhance wet-period rainfall prediction in the Northern Territory of Australia. Lagged large-scale climate indices, including the El Niño Southern Oscillation (ENSO), Dipole Mode Index (DMI), Madden-Julian Oscillation (MJO), and Interdecadal Pacific Oscillation (IPO), were employed as predictors for Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) models, both with and without wavelet transformation. Results indicate that hybrid models consistently outperformed their standalone counterparts, with the Wavelet-LSTM (W-LSTM) using Daubechies (db) wavelet emerging as the most effective configuration. Across three stations (Warruwi, Waterloo, and Avon Downs), the W-LSTM (db) achieved high correlations with observed rainfall (0.86–0.95) and substantially reduced RMSE, by up to 89.6% at Warruwi. It also demonstrated a strong ability to capture rainfall peaks and troughs, with correlations ranging from 0.76 to 0.91. Performance metrics further confirmed its superiority, with R² values of 0.80–0.89 and NSE values of 0.78–0.87, compared to the relatively lower performance of individual models (R² ranges 0.74–0.81). Although some challenges remain in accurately predicting extreme rainfall events, the overall findings present the advantages of combining wavelet preprocessing with machine learning. The W-LSTM (db) model provides a more robust and reliable approach to rainfall forecasting by improving the representation of variability and extremes. These results indicate the potential of hybrid wavelet-machine learning frameworks for enhancing predictive capability in regions influenced by high climate variability, with broader applicability to other tropical and monsoonal environments.

Graphical Abstract