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Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers

Author

Listed:
  • Tarate Suryakant Bajirao

    (Department of Soil and Water Conservation Engineering, G. B. Pant University of Agriculture and Technology, Pantnagar 263145, India)

  • Pravendra Kumar

    (Department of Soil and Water Conservation Engineering, G. B. Pant University of Agriculture and Technology, Pantnagar 263145, India)

  • Manish Kumar

    (Department of Soil and Water Conservation Engineering, G. B. Pant University of Agriculture and Technology, Pantnagar 263145, India)

  • Ahmed Elbeltagi

    (Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, Egypt
    College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China)

  • Alban Kuriqi

    (CERIS, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal)

Abstract

Estimating sediment flow rate from a drainage area plays an essential role in better watershed planning and management. In this study, the validity of simple and wavelet-coupled Artificial Intelligence (AI) models was analyzed for daily Suspended Sediment (SSC) estimation of highly dynamic Koyna River basin of India. Simple AI models such as the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were developed by supplying the original time series data as an input without pre-processing through a Wavelet (W) transform. The hybrid wavelet-coupled W-ANN and W-ANFIS models were developed by supplying the decomposed time series sub-signals using Discrete Wavelet Transform (DWT). In total, three mother wavelets, namely Haar, Daubechies, and Coiflets were employed to decompose original time series data into different multi-frequency sub-signals at an appropriate decomposition level. Quantitative and qualitative performance evaluation criteria were used to select the best model for daily SSC estimation. The reliability of the developed models was also assessed using uncertainty analysis. Finally, it was revealed that the data pre-processing using wavelet transform improves the model’s predictive efficiency and reliability significantly. In this study, it was observed that the performance of the Coiflet wavelet-coupled ANFIS model is superior to other models and can be applied for daily SSC estimation of the highly dynamic rivers. As per sensitivity analysis, previous one-day SSC (S t-1 ) is the most crucial input variable for daily SSC estimation of the Koyna River basin.

Suggested Citation

  • Tarate Suryakant Bajirao & Pravendra Kumar & Manish Kumar & Ahmed Elbeltagi & Alban Kuriqi, 2021. "Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers," Sustainability, MDPI, vol. 13(2), pages 1-29, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:542-:d:476862
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    References listed on IDEAS

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    1. Ahmadi, Mojgan & Etedali, Hadi Ramezani & Elbeltagi, Ahmed, 2021. "Evaluation of the effect of climate change on maize water footprint under RCPs scenarios in Qazvin plain, Iran," Agricultural Water Management, Elsevier, vol. 254(C).
    2. Priyanka Majumder & Arnab Paul & Pratik Saha & Mrinmoy Majumder & Dayarnab Baidya & Dhritiman Saha, 2023. "Trapezoidal fuzzy BWM-TOPSIS approach and application on water resources," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(3), pages 2648-2669, March.
    3. Elbeltagi, Ahmed & Azad, Nasrin & Arshad, Arfan & Mohammed, Safwan & Mokhtar, Ali & Pande, Chaitanya & Etedali, Hadi Ramezani & Bhat, Shakeel Ahmad & Islam, Abu Reza Md. Towfiqul & Deng, Jinsong, 2021. "Applications of Gaussian process regression for predicting blue water footprint: Case study in Ad Daqahliyah, Egypt," Agricultural Water Management, Elsevier, vol. 255(C).

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