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Multi-Step-Ahead Rainfall-Runoff Modeling: Decision Tree-Based Clustering for Hybrid Wavelet Neural- Networks Modeling

Author

Listed:
  • Amir Molajou

    (Iran University of Science and Technology)

  • Vahid Nourani

    (University of Tabriz
    University of Tabriz
    World Peace University)

  • Ali Davanlou Tajbakhsh

    (Khajeh Nasir al-Din Toosi University of Technology)

  • Hossein Akbari Variani

    (Iran University of Science & Technology)

  • Mina Khosravi

    (Iran University of Science & Technology)

Abstract

This paper introduces a novel hybrid approach for predicting the rainfall-runoff (r-r) phenomenon across different data division scenarios (50%-50%, 60%-40%, and 75%-25%) within two distinct watersheds, encompassing both monthly and daily scales. Additionally, the effectiveness of this newly proposed hybrid method is evaluated in multi-step ahead prediction (MSAP) scenarios. The proposed method comprises three primary steps. Initially, to address the non-stationarity of the runoff and rainfall time series, these series are decomposed into multiple sub-time series using the wavelet (WT) decomposition method. Subsequently, in the second step, the decomposed sub-series are utilized as input data for the M5 model tree, a decision tree-based model. The M5 model tree classifies the samples of decomposed runoff and rainfall time series into distinct classes. Finally, each class is modeled using an artificial neural network (ANN). The results demonstrate the superior efficiency of the proposed WT-M5-ANN method compared to other available hybrid methods. Specifically, the calculated R2 was 0.93 for the proposed WT-M5-ANN method, whereas it was 0.89 and 0.81 for the WT-ANN (WANN) and WT-M5 methods, respectively, for the Lobbs Hole Creek watershed at the daily scale.

Suggested Citation

  • Amir Molajou & Vahid Nourani & Ali Davanlou Tajbakhsh & Hossein Akbari Variani & Mina Khosravi, 2024. "Multi-Step-Ahead Rainfall-Runoff Modeling: Decision Tree-Based Clustering for Hybrid Wavelet Neural- Networks Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(13), pages 5195-5214, October.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:13:d:10.1007_s11269-024-03908-7
    DOI: 10.1007/s11269-024-03908-7
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    References listed on IDEAS

    as
    1. Xiao-Yun Chen & Kwok-Wing Chau, 2019. "Uncertainty Analysis on Hybrid Double Feedforward Neural Network Model for Sediment Load Estimation with LUBE Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(10), pages 3563-3577, August.
    2. Duong Tran Anh & Dat Vi Thanh & Hoang Minh Le & Bang Tran Sy & Ahad Hasan Tanim & Quoc Bao Pham & Thanh Duc Dang & Son T. Mai & Nguyen Mai Dang, 2023. "Effect of Gradient Descent Optimizers and Dropout Technique on Deep Learning LSTM Performance in Rainfall-runoff Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 639-657, January.
    3. 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.
    4. Zhangjun Liu & Jingwen Zhang & Tianfu Wen & Jingqing Cheng, 2022. "Uncertainty Quantification of Rainfall-runoff Simulations Using the Copula-based Bayesian Processor: Impacts of Seasonality, Copula Selection and Correlation Coefficient," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(13), pages 4981-4993, October.
    5. Donglai Li & Jingming Hou & Yangwei Zhang & Minpeng Guo & Dawei Zhang, 2022. "Influence of Time Step Synchronization on Urban Rainfall-Runoff Simulation in a Hybrid CPU/GPU 1D-2D Coupled Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3417-3433, August.
    6. Djerbouai Salim & Souag-Gamane Doudja & Ferhati Ahmed & Djoukbala Omar & Dougha Mostafa & Benselama Oussama & Hasbaia Mahmoud, 2023. "Comparative Study of Different Discrete Wavelet Based Neural Network Models for long term Drought Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1401-1420, February.
    7. Muhammad Shoaib & Asaad Y. Shamseldin & Sher Khan & Muhammad Sultan & Fiaz Ahmad & Tahir Sultan & Zakir Hussain Dahri & Irfan Ali, 2019. "Input Selection of Wavelet-Coupled Neural Network Models for Rainfall-Runoff Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(3), pages 955-973, February.
    8. 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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    More about this item

    Keywords

    Artificial neural network; Multi-step ahead predicting; M5 Model tree; Rainfall-runoff modeling; Wavelet transform;
    All these keywords.

    JEL classification:

    • M5 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics

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