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A Novel Hybrid Approach for Predicting Western Australia’s Seasonal Rainfall Variability

Author

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  • Farhana Islam

    (Swinburne University of Technology)

  • Monzur Alam Imteaz

    (Swinburne University of Technology)

Abstract

In this paper, 100 years of uninterrupted rainfall data for 12 rainfall stations (four rainfall stations from each region) in Western Australia were analyzed against respective dominant climate indices, and representative prediction models were developed using ARIMAX, GEP, and a hybrid technique (GEP-ARIMAX). Statistical performance evaluators such as Pearson correlation $$(r)$$ ( r ) , root mean square error $$(RMSE)$$ ( R M S E ) , mean absolute error ( $$MAE$$ MAE ), and refined Willmot index of agreement ( $${d}_{r}$$ d r ) were used to evaluate the prediction performance of the developed models. These models demonstrated their capability to predict up to four months in advance with Pearson correlation $$(r)$$ ( r ) values ranging from 0.53 to 0.83, 0.75 to 0.85, and 0.87 to 0.95 for ARIMAX, GEP, and hybrid (GEP-ARIMAX) models respectively. While compared, the hybrid (GEP-ARIMAX) model showed superior prediction performance in both calibration and validation periods with Pearson correlation $$(r)$$ ( r ) and refined Willmot index of agreement ( $${d}_{r}$$ d r ) values were as high as 0.96 and 0.84 respectively. This paper demonstrated a novel hybrid GEP-ARIMAX model showing significantly good rainfall forecasting capability than conventional linear and non-linear models.

Suggested Citation

  • Farhana Islam & Monzur Alam Imteaz, 2022. "A Novel Hybrid Approach for Predicting Western Australia’s Seasonal Rainfall Variability," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3649-3672, August.
  • Handle: RePEc:spr:waterr:v:36:y:2022:i:10:d:10.1007_s11269-022-03219-9
    DOI: 10.1007/s11269-022-03219-9
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    References listed on IDEAS

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    1. Saeed Mozaffari & Saman Javadi & Hamid Kardan Moghaddam & Timothy O. Randhir, 2022. "Forecasting Groundwater Levels using a Hybrid of Support Vector Regression and Particle Swarm Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 1955-1972, April.
    2. Anas Mahmood Al-Juboori, 2022. "Solving Complex Rainfall-Runoff Processes in Semi-Arid Regions Using Hybrid Heuristic Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 717-728, January.
    3. Adil M. Bagirov & Arshad Mahmood, 2018. "A Comparative Assessment of Models to Predict Monthly Rainfall in Australia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1777-1794, March.
    4. Saeid Mehdizadeh & Ali Kozekalani Sales, 2018. "A Comparative Study of Autoregressive, Autoregressive Moving Average, Gene Expression Programming and Bayesian Networks for Estimating Monthly Streamflow," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(9), pages 3001-3022, July.
    5. Meysam Ghamariadyan & Monzur A. Imteaz, 2021. "Prediction of Seasonal Rainfall with One-year Lead Time Using Climate Indices: A Wavelet Neural Network Scheme," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5347-5365, December.
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    Cited by:

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