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Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering

Author

Listed:
  • Rana Muhammad Adnan

    (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China)

  • Kulwinder Singh Parmar

    (Department of Mathematics, IKG Punjab Technical University, Jalandhar, Kapurthala 144603, India)

  • Salim Heddam

    (Agronomy Department, Hydraulics Division, Faculty of Science, University of Skikda, Skikda 21000, Algeria)

  • Shamsuddin Shahid

    (Faculty of Engineering, School of Civil Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia)

  • Ozgur Kisi

    (Faculty of Engineering, School of Civil Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia
    School of Technology, Ilia State University, Tbilisi 0162, Georgia)

Abstract

The accurate estimation of suspended sediments (SSs) carries significance in determining the volume of dam storage, river carrying capacity, pollution susceptibility, soil erosion potential, aquatic ecological impacts, and the design and operation of hydraulic structures. The presented study proposes a new method for accurately estimating daily SSs using antecedent discharge and sediment information. The novel method is developed by hybridizing the multivariate adaptive regression spline (MARS) and the Kmeans clustering algorithm (MARS–KM). The proposed method’s efficacy is established by comparing its performance with the adaptive neuro-fuzzy system (ANFIS), MARS, and M5 tree (M5Tree) models in predicting SSs at two stations situated on the Yangtze River of China, according to the three assessment measurements, RMSE, MAE, and NSE. Two modeling scenarios are employed; data are divided into 50–50% for model training and testing in the first scenario, and the training and test data sets are swapped in the second scenario. In Guangyuan Station, the MARS–KM showed a performance improvement compared to ANFIS, MARS, and M5Tree methods in term of RMSE by 39%, 30%, and 18% in the first scenario and by 24%, 22%, and 8% in the second scenario, respectively, while the improvement in RMSE of ANFIS, MARS, and M5Tree was 34%, 26%, and 27% in the first scenario and 7%, 16%, and 6% in the second scenario, respectively, at Beibei Station. Additionally, the MARS–KM models provided much more satisfactory estimates using only discharge values as inputs.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:4648-:d:540939
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    References listed on IDEAS

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    Cited by:

    1. Bibhuti Bhusan Sahoo & Sovan Sankalp & Ozgur Kisi, 2023. "A Novel Smoothing-Based Deep Learning Time-Series Approach for Daily Suspended Sediment Load Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(11), pages 4271-4292, September.
    2. Rana Muhammad Adnan Ikram & Xinyi Cao & Kulwinder Singh Parmar & Ozgur Kisi & Shamsuddin Shahid & Mohammad Zounemat-Kermani, 2023. "Modeling Significant Wave Heights for Multiple Time Horizons Using Metaheuristic Regression Methods," Mathematics, MDPI, vol. 11(14), pages 1-24, July.
    3. Rana Muhammad Adnan & Hong-Liang Dai & Reham R. Mostafa & Kulwinder Singh Parmar & Salim Heddam & Ozgur Kisi, 2022. "Modeling Multistep Ahead Dissolved Oxygen Concentration Using Improved Support Vector Machines by a Hybrid Metaheuristic Algorithm," Sustainability, MDPI, vol. 14(6), pages 1-23, March.
    4. Rong Cao & Xuehui Chen & Jianmin Jia & Hui Zhang, 2023. "Uncovering Equity and Travelers’ Behavior on the Expressway: A Case Study of Shandong, China," Sustainability, MDPI, vol. 15(11), pages 1-19, May.

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    More about this item

    Keywords

    estimating discharge–sediment relationship; MARS–Kmeans; MARS; ANFIS; M5 model tree;
    All these keywords.

    JEL classification:

    • M5 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics

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