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A Hybrid Model to Predict Monthly Streamflow Using Neighboring Rivers Annual Flows

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  • Anas Mahmood Al-Juboori

    (University of Mosul)

Abstract

The issue of predicting monthly streamflow data is an important issue in water resources engineering. In this paper, a hybrid model was proposed to generate monthly streamflow data for a river from the annual streamflow data. To develop the proposed hybrid model, a combination of K-Nearest Neighbor (KNN) and Random Tree (RT) algorithms was used. The hybrid structure was designed to predict the annual flow data for the target river using the annual flow data from neighboring rivers by applying the KNN model, and then generated the monthly flow data for the target river using the predicted annual flow by applying the random tree model. The hybrid model was applied to three rivers in Iraq. The accuracy of the proposed model was tested using two statistical indices, namely, the degree of determination and the efficiency coefficient. The results of the statistical indices indicated a good performance of the proposed hybrid model to generate monthly streamflow using annual streamflow data that the values of degree of determination and efficiency coefficient were greater than 0.91 in the training phase and greater than 0.79 in the validating phase.

Suggested Citation

  • Anas Mahmood Al-Juboori, 2021. "A Hybrid Model to Predict Monthly Streamflow Using Neighboring Rivers Annual Flows," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(2), pages 729-743, January.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:2:d:10.1007_s11269-020-02757-4
    DOI: 10.1007/s11269-020-02757-4
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    References listed on IDEAS

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    1. Taymoor Awchi, 2014. "River Discharges Forecasting In Northern Iraq Using Different ANN Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(3), pages 801-814, February.
    2. Anas Mahmood Al-Juboori, 2019. "Generating Monthly Stream Flow Using Nearest River Data: Assessing Different Trees Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3257-3270, July.
    3. Alpaslan Yarar, 2014. "A Hybrid Wavelet and Neuro-Fuzzy Model for Forecasting the Monthly Streamflow Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(2), pages 553-565, January.
    4. Shahab Araghinejad & Nima Fayaz & Seyed-Mohammad Hosseini-Moghari, 2018. "Development of a Hybrid Data Driven Model for Hydrological Estimation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(11), pages 3737-3750, September.
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    Cited by:

    1. 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.
    2. Manish Kumar & Ahmed Elbeltagi & Chaitanya B. Pande & Ali Najah Ahmed & Ming Fai Chow & Quoc Bao Pham & Anuradha Kumari & Deepak Kumar, 2022. "Applications of Data-driven Models for Daily Discharge Estimation Based on Different Input Combinations," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2201-2221, May.
    3. Jincheng Zhou & Dan Wang & Shahab S. Band & Changhyun Jun & Sayed M. Bateni & M. Moslehpour & Hao-Ting Pai & Chung-Chian Hsu & Rasoul Ameri, 2023. "Monthly River Discharge Forecasting Using Hybrid Models Based on Extreme Gradient Boosting Coupled with Wavelet Theory and Lévy–Jaya Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(10), pages 3953-3972, August.
    4. Wen-chuan Wang & Yu-jin Du & Kwok-wing Chau & Dong-mei Xu & Chang-jun Liu & Qiang Ma, 2021. "An Ensemble Hybrid Forecasting Model for Annual Runoff Based on Sample Entropy, Secondary Decomposition, and Long Short-Term Memory Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(14), pages 4695-4726, November.
    5. Maha Shabbir & Sohail Chand & Farhat Iqbal & Ozgur Kisi, 2024. "Hybrid Approach for Streamflow Prediction: LASSO-Hampel Filter Integration with Support Vector Machines, Artificial Neural Networks, and Autoregressive Distributed Lag Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(11), pages 4179-4196, September.

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