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Streamflow forecasting using a hybrid LSTM-PSO approach: the case of Seyhan Basin

Author

Listed:
  • Bulent Haznedar

    (Gaziantep University)

  • Huseyin Cagan Kilinc

    (Istanbul Aydın University)

  • Furkan Ozkan

    (Hasan Kalyoncu University)

  • Adem Yurtsever

    (İstanbul University-Cerrahpaşa
    Hasan Kalyoncu University)

Abstract

The conditions which affect the sustainability of water cause a number of serious environmental and hydrological problems. Effective and correct management of water resources constitutes an effective and important issue among scales. In this sense, a precise estimation of streamflow time series in rivers is one of the most important issues in optimal management of surface water resources. Therefore, a hybrid method combining particle swarm algorithm (PSO) and long short-term memory networks (LSTM) are proposed to predict flow with data obtained from different flow measurement stations. In this respect, the data gathered from three Flow Measurement Stations (FMS) from Zamanti and Eğlence rivers located on Seyhan Basin are utilized. Besides, the proposed LSTM-PSO method is compared to an adaptive neuro-fuzzy inference system (ANFIS) and the LSTM benchmark model to demonstrate the performance achievement of proposed method. The prediction performances of the developed hybrid model and the others are tested on the determined stations. The forecasting performances of the models are determined with RMSE, MAE, MAPE, SD, and R2 metrics. The comparison results indicated that the LSTM-PSO method provides highest results with values of R2 (≈ 0.9433), R2 (≈ 0.6972), and R2 (≈ 0.9273) for the Değirmenocağı, Eğribük, and Ergenusagi FMS data, respectively.

Suggested Citation

  • Bulent Haznedar & Huseyin Cagan Kilinc & Furkan Ozkan & Adem Yurtsever, 2023. "Streamflow forecasting using a hybrid LSTM-PSO approach: the case of Seyhan Basin," 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. 117(1), pages 681-701, May.
    • Handle: RePEc:spr:nathaz:v:117:y:2023:i:1:d:10.1007_s11069-023-05877-3
    DOI: 10.1007/s11069-023-05877-3
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    References listed on IDEAS

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    1. Fanping Zhang & Huichao Dai & Deshan Tang, 2014. "A Conjunction Method of Wavelet Transform-Particle Swarm Optimization-Support Vector Machine for Streamflow Forecasting," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-10, May.
    2. Mustafa Najat Asaad & Şule Eryürük & Kağan Eryürük, 2022. "Forecasting of Streamflow and Comparison of Artificial Intelligence Methods: A Case Study for Meram Stream in Konya, Turkey," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    3. Chujie Tian & Jian Ma & Chunhong Zhang & Panpan Zhan, 2018. "A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network," Energies, MDPI, vol. 11(12), pages 1-13, December.
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