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Prediction of Seasonal Rainfall with One-year Lead Time Using Climate Indices: A Wavelet Neural Network Scheme

Author

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  • Meysam Ghamariadyan

    (Swinburne University of Technology)

  • Monzur A. Imteaz

    (Swinburne University of Technology)

Abstract

This paper presents the development of the Wavelet Artificial Neural Networks (WANN) model to forecast seasonal rainfall in Queensland, Australia, using the Inter-decadal Pacific Oscillation (IPO), Southern Oscillation Index (SOI), and Nino3.4 climate indices as predictors. Eight input sets with different combinations of predictive variables from 1908 to 2016 were considered to develop forecast models for ten selected rainfall stations in Queensland, Australia. The outcomes of WANN modeling are compared with Artificial Neural Networks (ANN). Moreover, the skillfulness of the WANN in comparison to the current climate prediction system used by the Australian Community Climate Earth-System Simulator–Seasonal (ACCESS–S) and climatology forecasts are investigated. Besides, the WANN predictions are compared with two other conventional approaches like autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR) for further investigations. The comparisons indicated that the WANN achieves the lower average root mean square error (RMSE) in all the stations with 112.2mm compared to ANN with 178.9mm, ACCESS-S with 281.8mm, climatology prediction with 279.7mm, MLR with 195.1mm, and ARIMA with 187.7mm. The WANN seasonal rainfall forecasts are more accurate than the ANN, ACCESS-S, Climatology, MLR, and ARIMA by 37%, 60%, 53%, 42%, and 40%, respectively. It was also found that the ACCESS-S underestimates the extreme seasonal rainfall during the testing period up to 80%, while it is limited to 21% for the WANN among the selected stations. The results show that the WANN model outperforms the MLR, ARIMA, climatology, ACCESS-S, and ANN forecasts in all the selected stations.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:15:d:10.1007_s11269-021-03007-x
    DOI: 10.1007/s11269-021-03007-x
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    References listed on IDEAS

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    1. Kai Lun Chong & Sai Hin Lai & Yu Yao & Ali Najah Ahmed & Wan Zurina Wan Jaafar & Ahmed El-Shafie, 2020. "Performance Enhancement Model for Rainfall Forecasting Utilizing Integrated Wavelet-Convolutional Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(8), pages 2371-2387, June.
    2. Wossenu Abtew & Paul Trimble, 2010. "El Niño–Southern Oscillation Link to South Florida Hydrology and Water Management Applications," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(15), pages 4255-4271, December.
    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. Elnaz Sharghi & Vahid Nourani & Hessam Najafi & Amir Molajou, 2018. "Emotional ANN (EANN) and Wavelet-ANN (WANN) Approaches for Markovian and Seasonal Based Modeling of Rainfall-Runoff Process," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3441-3456, August.
    5. Lamine Diop & Saeed Samadianfard & Ansoumana Bodian & Zaher Mundher Yaseen & Mohammad Ali Ghorbani & Hana Salimi, 2020. "Annual Rainfall Forecasting Using Hybrid Artificial Intelligence Model: Integration of Multilayer Perceptron with Whale Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 733-746, January.
    6. Alireza Farrokhi & Saeed Farzin & Sayed-Farhad Mousavi, 2020. "A New Framework for Evaluation of Rainfall Temporal Variability through Principal Component Analysis, Hybrid Adaptive Neuro-Fuzzy Inference System, and Innovative Trend Analysis Methodology," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3363-3385, August.
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    Cited by:

    1. Ming Wei & Xue-yi You, 2022. "Monthly rainfall forecasting by a hybrid neural network of discrete wavelet transformation and deep learning," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4003-4018, September.
    2. Guo-Yu Huang & Chi-Ju Lai & Ping-Feng Pai, 2022. "Forecasting Hourly Intermittent Rainfall by Deep Belief Networks with Simple Exponential Smoothing," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(13), pages 5207-5223, October.
    3. 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.

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