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Solving Complex Rainfall-Runoff Processes in Semi-Arid Regions Using Hybrid Heuristic Model

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  • Anas Mahmood Al-Juboori

    (University of Mosul)

Abstract

In the current research, a hybrid model was proposed to solve the complexity of rainfall-runoff models in semi-arid regions. The proposed hybrid model structure consists of linking two data mining models, namely, Group Method of Data Handling (GMDH) and Generalized Linear Model (GLM). The proposed hybrid model structure consists of two phases. The GMDH model was used in the first phase of the hybrid model to predict daily streamflow. The first phase consists of two sections. In the first section a predictive model is developed using the time series of the daily streamflow. In the second section the rainfall-runoff model was developed. The outputs of the first phase of the hybrid model are used as inputs to the second phase of the hybrid model. The second phase of the hybrid model was developed using the GLM model. The Gomel River in Iraq was selected as a case study. The daily rainfall data and daily streamflow data for the period from January 1, 2004 to December 19, 2016 were used to train and validate the model. The results proved the accuracy of the proposed hybrid model in estimating the daily streamflow of the study area, where the value of R2 was 0.92 in the training period and 0.88 in the validation period of the model.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:2:d:10.1007_s11269-021-03053-5
    DOI: 10.1007/s11269-021-03053-5
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    References listed on IDEAS

    as
    1. 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.
    2. Erhao Meng & Shengzhi Huang & Qiang Huang & Wei Fang & Hao Wang & Guoyong Leng & Lu Wang & Hao Liang, 2021. "A Hybrid VMD-SVM Model for Practical Streamflow Prediction Using an Innovative Input Selection Framework," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(4), pages 1321-1337, March.
    3. A. B. Dariane & M. Farhani & Sh Azimi, 2018. "Long Term Streamflow Forecasting Using a Hybrid Entropy Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(4), pages 1439-1451, March.
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    Cited by:

    1. 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.
    2. 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.

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