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A Hybrid ANFIS-GA Approach for Estimation of Hydrological Time Series

Author

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  • Bulent Haznedar

    (Gaziantep University)

  • Huseyin Cagan Kilinc

    (Istanbul Esenyurt University)

Abstract

River flow modeling plays a leading role in the management of water resources and ensuring sustainability. The complex nature of hydrological systems and the difficulty in the application process have led researchers to seek more instantaneous methods for flow predictions. Therefore, with the development of artificial intelligence-based techniques, hybrid modeling has become popular among hydrologists in recent years. For that reason, this study seeks to develop a hybrid model that integrates an adaptive neuro-fuzzy inference system (ANFIS) with a genetic algorithm (GA) to predict river flow. Fundamentally, the performance of an ANFIS model depends on the optimum model parameters. Thus, it is aimed to increase the prediction performance by optimizing the ANFIS parameters with the population-based GA algorithm, which is a powerful algorithm. In this respect, the data gathered from Zamanti and Körkün Flow Measurement Stations (FMS) of Seyhan River, one of Turkey's significant rivers, were employed. Besides, the proposed hybrid ANFIS-GA approach was compared to classical ANFIS model to demonstrate the improvement of its performance. Also, within the scope of simulation studies, the traditional artificial neural networks (ANN) and the long-short term memory (LSTM) method which is a quite popular in recent years were used to predict streamflow data. The estimation results of the models were evaluated with RMSE, MAE, MAPE, SD, and R2 statistical metrics. In a nutshell, the outcomes indicated that the proposed ANFIS-GA method was the most successful model by achieving the highest values of R2 (≈0.9409) and R2 (≈0.9263) for the Zamanti and Körkün FMS data.

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  • Bulent Haznedar & Huseyin Cagan Kilinc, 2022. "A Hybrid ANFIS-GA Approach for Estimation of Hydrological Time Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4819-4842, September.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:12:d:10.1007_s11269-022-03280-4
    DOI: 10.1007/s11269-022-03280-4
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    References listed on IDEAS

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    1. Khabat Khosravi & Ali Golkarian & John P. Tiefenbacher, 2022. "Using Optimized Deep Learning to Predict Daily Streamflow: A Comparison to Common Machine Learning Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 699-716, January.
    2. Yongtao Wang & Jian Liu & Rong Li & Xinyu Suo & EnHui Lu, 2022. "Medium and Long-term Precipitation Prediction Using Wavelet Decomposition-prediction-reconstruction Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(3), pages 971-987, February.
    3. Hui Hu & Jianfeng Zhang & Tao Li, 2021. "A Novel Hybrid Decompose-Ensemble Strategy with a VMD-BPNN Approach for Daily Streamflow Estimating," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5119-5138, December.
    4. Seyed Akrami & Vahid Nourani & S. Hakim, 2014. "Development of Nonlinear Model Based on Wavelet-ANFIS for Rainfall Forecasting at Klang Gates Dam," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(10), pages 2999-3018, August.
    5. Ozgur Kisi & Levent Latifoğlu & Fatma Latifoğlu, 2014. "Investigation of Empirical Mode Decomposition in Forecasting of Hydrological Time Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 4045-4057, September.
    6. Mojtaba Poursaeid & Amir Houssain Poursaeid & Saeid Shabanlou, 2022. "A Comparative Study of Artificial Intelligence Models and A Statistical Method for Groundwater Level Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(5), pages 1499-1519, March.
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