IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v38y2024i13d10.1007_s11269-024-03908-7.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-024-03908-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-024-03908-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    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. 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.
    9. 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.
    10. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Nourani, Vahid & Sharghi, Elnaz & Behfar, Nazanin & Zhang, Yongqiang, 2022. "Multi-step-ahead solar irradiance modeling employing multi-frequency deep learning models and climatic data," Applied Energy, Elsevier, vol. 315(C).
    3. Maher Abuhussain & Ahmad Baghdadi, 2024. "A Novel Framework for Estimation of the Maintenance and Operation Cost in Construction Projects: A Step Toward Sustainable Buildings," Sustainability, MDPI, vol. 16(23), pages 1-21, November.
    4. Vahid Nourani & Nardin Jabbarian Paknezhad & Hitoshi Tanaka, 2021. "Prediction Interval Estimation Methods for Artificial Neural Network (ANN)-Based Modeling of the Hydro-Climatic Processes, a Review," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    5. Maher Awad Abuhussain, 2024. "Integrated Fuzzy Technique for Order Preference by Similarity to Ideal Solution and Emotional Artificial Neural Network Model for Comprehensive Risk Prioritization in Green Construction Projects," Sustainability, MDPI, vol. 16(22), pages 1-23, November.
    6. Wongchai Anupong & Muhsin Jaber Jweeg & Sameer Alani & Ibrahim H. Al-Kharsan & Aníbal Alviz-Meza & Yulineth Cárdenas-Escrocia, 2023. "Comparison of Wavelet Artificial Neural Network, Wavelet Support Vector Machine, and Adaptive Neuro-Fuzzy Inference System Methods in Estimating Total Solar Radiation in Iraq," Energies, MDPI, vol. 16(2), pages 1-14, January.
    7. 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.
    8. Rafael González-Val, 2021. "The Probability Distribution of Worldwide Forest Areas," Sustainability, MDPI, vol. 13(3), pages 1-19, January.
    9. Muhammad Asif & Monique M. Kuglitsch & Ivanka Pelivan & Raffaele Albano, 2025. "Review and Intercomparison of Machine Learning Applications for Short-term Flood Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(5), pages 1971-1991, March.
    10. Mina Khosravi & Abbas Afshar & Amir Molajou, 2022. "Decision Tree-Based Conditional Operation Rules for Optimal Conjunctive Use of Surface and Groundwater," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 2013-2025, April.
    11. Harshanth Balacumaresan & Monzur Alam Imteaz & Md Abdul Aziz & Tanveer Choudhury, 2024. "Use of Artificial Intelligence Modelling for the Dynamic Simulation of Urban Catchment Runoff," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(10), pages 3657-3683, August.
    12. Kouao Laurent Kouadio & Jianxin Liu & Serge Kouamelan Kouamelan & Rong Liu, 2023. "Ensemble Learning Paradigms for Flow Rate Prediction Boosting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(11), pages 4413-4431, September.
    13. Cheng, Ziwei & Yao, Zhen, 2024. "A novel approach to predict buildings load based on deep learning and non-intrusive load monitoring technique, toward smart building," Energy, Elsevier, vol. 312(C).
    14. Tarate Suryakant Bajirao & Pravendra Kumar & Manish Kumar & Ahmed Elbeltagi & Alban Kuriqi, 2021. "Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers," Sustainability, MDPI, vol. 13(2), pages 1-29, January.
    15. Kiyoumars Roushangar & Sadegh Abdelzad, 2023. "River Flow Modeling in Semi-Arid and Humid Regions Using an Integrated Method Based on LARS-WG and LSTM Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(10), pages 3813-3831, August.
    16. Xinxin Pan & Jingming Hou & Tian Wang & Xinyi Li & Jing Jing & Guangzhao Chen & Juan Qiao & Qingyuan Guo, 2024. "Study on the Influence of Temporal and Spatial Resolution of Rainfall Data on Watershed Flood Simulation Performance," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(8), pages 2647-2668, June.
    17. Emine Dilek Taylan, 2024. "An Approach for Future Droughts in Northwest Türkiye: SPI and LSTM Methods," Sustainability, MDPI, vol. 16(16), pages 1-22, August.
    18. José-Luis Molina & Santiago Zazo & Ana-María Martín-Casado & María-Carmen Patino-Alonso, 2020. "Rivers’ Temporal Sustainability through the Evaluation of Predictive Runoff Methods," Sustainability, MDPI, vol. 12(5), pages 1-21, February.
    19. Abdol Rassoul Zarei & Mohammad Reza Mahmoudi & Alireza Pourbagheri, 2024. "Meteorological Drought Prediction Based on Evaluating the Efficacy of Several Prediction Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(7), pages 2601-2625, May.
    20. Mohammed H. Alshareef & Bassam M. Aljahdali & Ayman F. Alghanmi & Hussain T. Sulaimani, 2024. "Spatial Analysis and Risk Evaluation for Port Crisis Management Using Integrated Soft Computing and GIS-Based Models: A Case Study of Jazan Port, Saudi Arabia," Sustainability, MDPI, vol. 16(12), pages 1-24, June.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:38:y:2024:i:13:d:10.1007_s11269-024-03908-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.