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Flood prediction based on climatic signals using wavelet neural network

  • Research Article - Hydrology
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Abstract

Large-scale climatic circulation modulates the weather patterns around the world. Understanding the teleconnections between large-scale circulation and local hydro-climatological variables has been a major thrust area of hydro-climatology research. The large-scale circulation is often quantified in terms of sea surface temperature (SST) and sea-level pressure (SLP). In this paper, we investigate the potential of wavelet neural network (WNN) hybrid model to predict maximum monthly discharge of the Madarsoo watershed, North of Iran considering two large-scale climatic signals like SST and SLP as inputs. Error measures like root-mean-square error (RMSE), and mean absolute error along with the correlation measures like coefficient of correlation (R), and Nash–Sutcliffe coefficient (CNS) were used to quantify the performance of prediction of maximum monthly discharge of three different hydrometry stations of the watershed. In all the cases, the WNN hybrid machine learning model was found to be giving superior performance consistently against the standalone artificial neural network (ANN) model and multiple linear regression model to predict the flood discharges of March and August months. The prediction of flood for August which is more devastating is found to be slightly better than the prediction of floods of March, in the stations served with smaller drainage area. The RMSE, R and CNS of Tamer hydrometry station in August were found to be 0.68, 0.996, and 0.99 m3/s, respectively, for the test period by using WNN model against 1.55, 0.989 and 0.95 by ANN model. Moreover, when evaluated for predicting the maximum monthly discharge in March and August between 2012 and 2013, the wavelet-based neural networks performed remarkably well than the ANN.

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Authors and Affiliations

  1. Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Vietnam

    Nguyen Thi Thuy Linh

  2. Civil Engineering Department, Shahrood University of Technology, Shahrud, Iran

    Hossein Ruigar & Saeed Golian

  3. ICARUS, Department of Geography, Maynooth University, Maynooth, Ireland

    Saeed Golian

  4. Department of Geology, School of Earth Sciences, Bahir Dar University, P.O. Box 79, Bahir Dar, Ethiopia

    Getnet Taye Bawoke

  5. Department of Hydrology, Indian Institute of Technology, Roorkee, India

    Vivek Gupta

  6. State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, 100084, China

    Khalil Ur Rahman

  7. Department of Civil Engineering, TKM College of Engineering, Kollam, 691005, India

    Adarsh Sankaran

  8. Institute of Applied Technology, Thu Dau Mot University, Thu Dau Mot City, Binh Duong Province, Vietnam

    Quoc Bao Pham

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  1. Nguyen Thi Thuy Linh
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  2. Hossein Ruigar
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  3. Saeed Golian
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  4. Getnet Taye Bawoke
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Correspondence to Quoc Bao Pham.

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Communicated by Michael Nones, Ph.D. (CO-EDITOR-IN-CHIEF) / Achilleas G. Samaras, Ph.D. (ASSOCIATE EDITOR).

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Linh, N.T.T., Ruigar, H., Golian, S. et al. Flood prediction based on climatic signals using wavelet neural network. Acta Geophys. 69, 1413–1426 (2021). https://doi.org/10.1007/s11600-021-00620-7

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