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A Comparative Analysis of Data-Driven Models (SVR, ANFIS, and ANNs) for Daily Karst Spring Discharge Prediction

Author

Listed:
  • Akram Rahbar

    (Kharazmi University)

  • Ali Mirarabi

    (Water Resources Management Company (WRM))

  • Mohammad Nakhaei

    (Kharazmi University)

  • Mahdi Talkhabi

    (Kharazmi University)

  • Maryam Jamali

    (Kharazmi University)

Abstract

Spring discharge always illustrates the groundwater-flux and aquifer storage oscillations. Because of inherent heterogeneity in karst environments, it is essential to mimic karst spring flows to acquire a superior understanding of hydrological processes and provide sustainable management and protection of karst waters. The framework of karst media is nonlinear and complex, which can be demonstrated by data-driven models. In this study, the performance of Support Vector Regression (SVR), the Adaptive Neuro-Fuzzy Inference System (ANFIS), and Artificial Neural Networks (ANN) was assessed to predict spring discharge 1-, 3-, 7-, 10- and 14-day ahead. A hybrid Gamma Test-Genetic Algorithm was performed to establish an optimal input combination. SVR, ANFIS, and ANN performances were analyzed via four residuals: Correlation Coefficient, Mean Absolute Error, Root Mean Squared Error (RMSE), Nash–Sutcliffe Efficiency, and Developed Discrepancy Ratio. According to the RMSE values (of 0.08, 0.18, 0.64 and 0.86 using ANN; 0.19, 0.22, 0.83, and 0.61 using ANFIS; and 0.15, 0.26, 0.78 and 0.59 using SVR for Lordegan, Deime, Dehcheshmeh, and Dehghara springs, respectively), the results demonstrated that ANN was highly accurate for the discharge prediction of the Lordegan, Deime, and Dehcheshme springs whereas it had the least accuracy for the discharge prediction of the Dehghara spring up to 14-day ahead. However, SVR performed better than the other models for all prediction steps in the Dehghara spring, having a more complex and heterogeneous flow system compared to the others. For all the springs, the models’ accuracy decreased as the time ahead increased.

Suggested Citation

  • Akram Rahbar & Ali Mirarabi & Mohammad Nakhaei & Mahdi Talkhabi & Maryam Jamali, 2022. "A Comparative Analysis of Data-Driven Models (SVR, ANFIS, and ANNs) for Daily Karst Spring Discharge Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 589-609, January.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:2:d:10.1007_s11269-021-03041-9
    DOI: 10.1007/s11269-021-03041-9
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    References listed on IDEAS

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    Cited by:

    1. Zahra Dashti & Mohammad Nakhaei & Meysam Vadiati & Gholam Hossein Karami & Ozgur Kisi, 2023. "Estimation of Unconfined Aquifer Transmissivity Using a Comparative Study of Machine Learning Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(12), pages 4909-4931, September.
    2. Hao Yang & Weide Li, 2023. "Data Decomposition, Seasonal Adjustment Method and Machine Learning Combined for Runoff Prediction: A Case Study," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 557-581, January.
    3. Saeed Mozaffari & Saman Javadi & Hamid Kardan Moghaddam & Timothy O. Randhir, 2022. "Forecasting Groundwater Levels using a Hybrid of Support Vector Regression and Particle Swarm Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 1955-1972, April.

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