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A new soft computing model for daily streamflow forecasting

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Abstract

Accurate stream flow quantification and prediction are essential for the local and global planning and management of basins to cope with climate change. The ability to forecast streamflow is crucial, as it can help mitigate flood risks. Long-term stream flow data records are needed for hydropower plant construction, flood prediction, watershed management, and long-term water supply use. An accurate assessment of streamflow is considered as very challenging and critical tasks. A new predicting model is developed in this research, combining the technique of sunflower optimization (SFA) as an evolutionary algorithm with the multi-layer perceptron (MLP) algorithm to predict streamflow in Malaysia's Jam Seyed Omar (JSO) and Muda Di Jeniang (MDJ) stations. Principal component analysis (PCA) was performed on Q (t) (t: the number of the current day) before model creation to pick essential inputs for a maximum of 6 lags. With the classical MLP and two other hybrid MLP models (MLP-particle swarm optimization (MLP-PSO) and MLP-genetic algorithm (MLP-GA)), the results of the MLP-sunflower algorithm (SFA) were benchmarked. As compared to other models, the MLP-SFA could be able to reduce the Root Mean Square Error (RMSE) by a value of between 12 and 21% at the JSO station and between 8 and 24% at the MDJ station. In conclusion, this research found that combining MLP with optimization algorithms improved the precision of the stand-alone MLP model, with SFA integration being the most efficient.

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Authors and Affiliations

  1. Department of Civil Engineering, College of Engineering, University of Diyala, Diyala Governorate, Iraq

    Saad Sh. Sammen

  2. Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran

    Mohammad Ehteram

  3. Faculty of Engineering, Department of Civil Engineering, Baze University, Abuja, Nigeria

    S. I. Abba

  4. Department of Electrical and Electronic, Kano University of Science and Technology, Wudil, Nigeria

    R. A. Abdulkadir

  5. Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN), 43000, Kajang, Selangor, Malaysia

    Ali Najah Ahmed

  6. Civil Engineering Department, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia

    Ahmed El-Shafie

Authors
  1. Saad Sh. Sammen
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  2. Mohammad Ehteram
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  3. S. I. Abba
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  4. R. A. Abdulkadir
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  5. Ali Najah Ahmed
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  6. Ahmed El-Shafie
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Correspondence to Saad Sh. Sammen.

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Sammen, S.S., Ehteram, M., Abba, S.I. et al. A new soft computing model for daily streamflow forecasting. Stoch Environ Res Risk Assess 35, 2479–2491 (2021). https://doi.org/10.1007/s00477-021-02012-1

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