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Regional Flood Frequency Analysis Through Some Machine Learning Models in Semi-arid Regions

Author

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  • Pezhman Allahbakhshian-Farsani

    (Tarbiat Modares University)

  • Mehdi Vafakhah

    (Tarbiat Modares University)

  • Hadi Khosravi-Farsani

    (Shahrekord University)

  • Elke Hertig

    (University of Augsburg)

Abstract

The machine learning models (MLMs), including support vector regression (SVR), multivariate adaptive regression spline (MARS), boosted regression trees (BRT), and projection pursuit regression (PPR) are compared to traditional method i.e. nonlinear regression (NLR) in regional flood frequency analysis (RFFA). In this study, the Karun and Karkheh watersheds, which is located in the southwestern of Iran, with the same climatic and physiographic conditions are considered. Fifty-four hydrometric stations with a period of 21 years (1993–2013) are selected based on the instructions of U.S. Federal Agencies Bulletin 17 B were applied for RFFA. The generalized normal (GNO) probability distribution function (PDF) is selected by the L-moment method among five PDFs, including the GNO, generalized Pareto (GP), generalized logistic (GL), generalized extreme value (GEV) and Pearson type 3 (P ІІІ) to estimate flood discharge for the expected return periods. Twenty-five predictor variables, such as physiographic, climatologic, geologic, soil and land use variables are extracted. Follow land, maximum 24-h rainfall, mean watershed slope, compactness coefficient, mean and maximum watershed elevation variables are recognized as the appropriate combination of input using gamma test (GT). The overall results indicate that the SVR, PPR, and MARS models in comparison to the NLR and BRT models have a better performance to estimate flood discharge with the expected return periods. Future, the SVR model based on radial basis function (RBF) kernel is chosen as the best model in terms of the mean of the Nash-Sutcliff coefficient (M-Ef) and the mean of relative root mean squared error (M-RMSEr) (i.e. 0.94 and 63.93, respectively) for different return periods.

Suggested Citation

  • Pezhman Allahbakhshian-Farsani & Mehdi Vafakhah & Hadi Khosravi-Farsani & Elke Hertig, 2020. "Regional Flood Frequency Analysis Through Some Machine Learning Models in Semi-arid Regions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2887-2909, July.
    • Handle: RePEc:spr:waterr:v:34:y:2020:i:9:d:10.1007_s11269-020-02589-2
    DOI: 10.1007/s11269-020-02589-2
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    References listed on IDEAS

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    1. Felício Cassalho & Samuel Beskow & Carlos Rogério Mello & Maíra Martim Moura & Laura Kerstner & Leo Fernandes Ávila, 2018. "At-Site Flood Frequency Analysis Coupled with Multiparameter Probability Distributions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 285-300, January.
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    6. Mojtaba Sadegh & Najmeh Mahjouri & Reza Kerachian, 2010. "Optimal Inter-Basin Water Allocation Using Crisp and Fuzzy Shapley Games," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(10), pages 2291-2310, August.
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    2. Arash Adib & Arash Zaerpour & Ozgur Kisi & Morteza Lotfirad, 2021. "A Rigorous Wavelet-Packet Transform to Retrieve Snow Depth from SSMIS Data and Evaluation of its Reliability by Uncertainty Parameters," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2723-2740, July.
    3. Shuhui Guo & Lihua Xiong & Jie Chen & Shenglian Guo & Jun Xia & Ling Zeng & Chong-Yu Xu, 2023. "Nonstationary Regional Flood Frequency Analysis Based on the Bayesian Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 659-681, January.
    4. Rabin Chakrabortty & Subodh Chandra Pal & Saeid Janizadeh & M. Santosh & Paramita Roy & Indrajit Chowdhuri & Asish Saha, 2021. "Impact of Climate Change on Future Flood Susceptibility: an Evaluation Based on Deep Learning Algorithms and GCM Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4251-4274, September.
    5. Nilufa Afrin & Farhad Ahamed & Ataur Rahman, 2024. "Development of a convolutional neural network based regional flood frequency analysis model for South-east Australia," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(12), pages 11349-11376, September.

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