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Performance Enhancement Model for Rainfall Forecasting Utilizing Integrated Wavelet-Convolutional Neural Network

Author

Listed:
  • Kai Lun Chong

    (University of Malaya)

  • Sai Hin Lai

    (University of Malaya
    Changsha University of Science and Technology)

  • Yu Yao

    (Changsha University of Science and Technology)

  • Ali Najah Ahmed

    (Universiti Tenaga, Nasional (UNITEN))

  • Wan Zurina Wan Jaafar

    (University of Malaya)

  • Ahmed El-Shafie

    (University of Malaya)

Abstract

The core objective of this study is to carry out rainfall forecasting over the Langat River Basin through the integration of wavelet transform (WT) and convolutional neural network (CNN). The proposed method involves using CNN for feature extraction to efficiently learn from the raw rainfall dataset. With the aid of deep architecture, a highly abstracted representation of the inputs time series with a high level of interpretation is formed at each subsequent CNN layer. The use of WT in forecasting the rainfall time series is by preprocessing the raw rainfall dataset into a set of decomposed wavelet components as inputs for the CNN model using discrete wavelet transform (DWT). The conditions for discretizing the raw input through DWT are discussed, along with the criteria to be used. Daily datasets, ranging from January 2002 to December 2017, were used. The results showed that the proposed model could satisfactorily capture patterns of the rainfall time series, for both monthly rainfalls forecasting or daily rainfall forecasting. Three performance indices were used to evaluate the model accuracy: RMSE, RSR, and MAE. These statistical indices have a range of value from 0 to a finite value that depends on the scale of the number used. In general, a lower value is better than a higher one.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:34:y:2020:i:8:d:10.1007_s11269-020-02554-z
    DOI: 10.1007/s11269-020-02554-z
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    References listed on IDEAS

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    1. Jayashree Chadalawada & Vojtech Havlicek & Vladan Babovic, 2017. "A Genetic Programming Approach to System Identification of Rainfall-Runoff Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(12), pages 3975-3992, September.
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    4. Kai Lun Chong & Sai Hin Lai & Ahmed El-Shafie, 2019. "Wavelet Transform Based Method for River Stream Flow Time Series Frequency Analysis and Assessment in Tropical Environment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(6), pages 2015-2032, April.
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    Cited by:

    1. Mahdi Valikhan Anaraki & Saeed Farzin & Sayed-Farhad Mousavi & Hojat Karami, 2021. "Uncertainty Analysis of Climate Change Impacts on Flood Frequency by Using Hybrid Machine Learning Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 199-223, January.
    2. Juliano Santos Finck & Olavo Correa Pedrollo, 2021. "Facing Losses of Telemetric Signal in Real Time Forecasting of Water Level using Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 1119-1133, February.
    3. Xingsheng Shu & Wei Ding & Yong Peng & Ziru Wang & Jian Wu & Min Li, 2021. "Monthly Streamflow Forecasting Using Convolutional Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5089-5104, December.
    4. Radhikesh Kumar & Maheshwari Prasad Singh & Bishwajit Roy & Afzal Hussain Shahid, 2021. "A Comparative Assessment of Metaheuristic Optimized Extreme Learning Machine and Deep Neural Network in Multi-Step-Ahead Long-term Rainfall Prediction for All-Indian Regions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1927-1960, April.
    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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