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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

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    1. Keivan Karimizadeh & Jaeeung Yi, 2023. "Modeling Hydrological Responses of Watershed Under Climate Change Scenarios Using Machine Learning Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(13), pages 5235-5254, October.
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    10. 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.
    11. Amir Molajou & Vahid Nourani & Abbas Afshar & Mina Khosravi & Adam Brysiewicz, 2021. "Optimal Design and Feature Selection by Genetic Algorithm for Emotional Artificial Neural Network (EANN) 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. 35(8), pages 2369-2384, June.
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    1. Anas Rahimi & Noor Kh. Yashooa & Ali Najah Ahmed & Mohsen Sherif & Ahmed El-shafie, 2025. "Different Time-Increment Rainfall Prediction Models: a Machine Learning Approach Using Various Input Scenarios," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(4), pages 1677-1696, March.

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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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