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Evaluating Different Machine Learning Models for Runoff and Suspended Sediment Simulation

Author

Listed:
  • Ashish Kumar

    (G.B. Pant University of Agriculture and Technology)

  • Pravendra Kumar

    (G.B. Pant University of Agriculture and Technology)

  • Vijay Kumar Singh

    (G.B. Pant University of Agriculture and Technology)

Abstract

In the present study, prediction of runoff and sediment at Polavaram and Pathagudem sites of the Godavari basin was carried out using machine learning models such as artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Different combinations of antecedent stage, current day stage and antecedent runoff for current day runoff prediction and antecedent runoff, current day runoff and antecedent sediment for current day sediment prediction were explored using Gamma test (GT) to select the effective input variables for runoff and sediment prediction. The performance during training and testing periods of the ANN and ANFIS models were evaluated quantitatively through various statistical indices and qualitative by visual observation. After comparing the qualified results of different ANN and ANFIS models it was found that ANN model with double hidden layers and ANFIS model with membership function (Triangular, 3) performed well for runoff and sediment predictions, respectively for Pathagudem site. ANFIS model with membership function (Triangular, 3) and ANFIS model with membership function (Gaussian, 3) shown the best results for runoff and sediment prediction, respectively, for Polavaram site. The effect of input variables on the selected models was also validated by the way of sensitivity analysis. The results of sensitivity analysis was found that the current day runoff mostly depends on present day stage and present day sediment depends on current day runoff.

Suggested Citation

  • Ashish Kumar & Pravendra Kumar & Vijay Kumar Singh, 2019. "Evaluating Different Machine Learning Models for Runoff and Suspended Sediment Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(3), pages 1217-1231, February.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:3:d:10.1007_s11269-018-2178-z
    DOI: 10.1007/s11269-018-2178-z
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    References listed on IDEAS

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    1. Isa Ebtehaj & Hossein Bonakdari, 2014. "Performance Evaluation of Adaptive Neural Fuzzy Inference System for Sediment Transport in Sewers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4765-4779, October.
    2. Meral Buyukyildiz & Serife Yurdagul Kumcu, 2017. "An Estimation of the Suspended Sediment Load Using Adaptive Network Based Fuzzy Inference System, Support Vector Machine and Artificial Neural Network Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(4), pages 1343-1359, March.
    3. 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.
    4. Ozgur Kisi & Mohammad Zounemat-Kermani, 2016. "Suspended Sediment Modeling Using Neuro-Fuzzy Embedded Fuzzy c-Means Clustering Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(11), pages 3979-3994, September.
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    Cited by:

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    3. Rana Muhammad Adnan & Kulwinder Singh Parmar & Salim Heddam & Shamsuddin Shahid & Ozgur Kisi, 2021. "Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering," Sustainability, MDPI, vol. 13(9), pages 1-21, April.
    4. Laís Coelho Teixeira & Priscila Pacheco Mariani & Olavo Correa Pedrollo & Nilza Maria Castro & Vanessa Sari, 2020. "Artificial Neural Network and Fuzzy Inference System Models for Forecasting Suspended Sediment and Turbidity in Basins at Different Scales," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(11), pages 3709-3723, September.
    5. Avay Risal & Prem B. Parajuli, 2022. "Evaluation of the Impact of Best Management Practices on Streamflow, Sediment and Nutrient Yield at Field and Watershed Scales," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(3), pages 1093-1105, February.
    6. Gaurav Singh & A. R. S. Kumar & R. K. Jaiswal & Surjeet Singh & R. M. Singh, 2022. "Model coupling approach for daily runoff simulation in Hamp Pandariya catchment of Chhattisgarh state in India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(10), pages 12311-12339, October.
    7. Lingqi Li & Kai Wu & Enhui Jiang & Huijuan Yin & Yuanjian Wang & Shimin Tian & Suzhen Dang, 2021. "Evaluating Runoff-Sediment Relationship Variations Using Generalized Additive Models That Incorporate Reservoir Indices for Check Dams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(11), pages 3845-3860, September.
    8. Mojtaba Kadkhodazadeh & Saeed Farzin, 2021. "A Novel LSSVM Model Integrated with GBO Algorithm to Assessment of Water Quality Parameters," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 3939-3968, September.
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