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Estimation of Suspended Sediment Concentration by M5 Model Tree Based on Hydrological and Moderate Resolution Imaging Spectroradiometer (MODIS) Data

Author

Listed:
  • Javad Zahiri

    (Agricultural Sciences and Natural Resources University of Khuzestan)

  • Zeynab Mollaee

    (Agricultural Sciences and Natural Resources University of Khuzestan)

  • Mohammad Reza Ansari

    (Agricultural Sciences and Natural Resources University of Khuzestan)

Abstract

The Estimation of suspended sediment concentration (SSC) is an important factor in river engineering, which is used as an indicator of land-use change, water quality studies, and all projects related to constructions in rivers. In this research, the M5 model tree and the Moderate Resolution Imaging Spectroradiometer (MODIS) data were utilized to estimate the SSC at Ahvaz station on the Karun River. In this study, 135 cloud-free images of the MODIS sensor on the Terra satellite were taken for days corresponding to field SSC data, during the years 2000 to 2015. Input parameters of the model tree in this study were flow discharge, derived from hydrological data, and red (R), near-infrared (NIR) bands, and NIR/R ratio extracted from MODIS imagery. The results of statistical analysis illustrate that the M5 model outperforms the sediment rating curve (SRC) method, which is the most common method of estimating suspended sediment load. The Nash-Sutcliffe efficiency index for the M5 model tree of 0.58 was achieved, which was much better than that of the SRC method (0.26). At high fluxes, the efficiency of the SRC method significantly reduced, while the model tree provides acceptable results. The global sensitivity analysis on the M5 model pointed out that 93% of output variance was established by the main effects of input parameters, and less than 7% belong to the interaction effects. 73% and 12% of output variance specified by the main effects of flow discharge and NIR/R ratio, respectively.

Suggested Citation

  • Javad Zahiri & Zeynab Mollaee & Mohammad Reza Ansari, 2020. "Estimation of Suspended Sediment Concentration by M5 Model Tree Based on Hydrological and Moderate Resolution Imaging Spectroradiometer (MODIS) Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(12), pages 3725-3737, September.
    • Handle: RePEc:spr:waterr:v:34:y:2020:i:12:d:10.1007_s11269-020-02577-6
    DOI: 10.1007/s11269-020-02577-6
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    Citations

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    Cited by:

    1. Elham Ghanbari-Adivi & Mohammad Ehteram & Alireza Farrokhi & Zohreh Sheikh Khozani, 2022. "Combining Radial Basis Function Neural Network Models and Inclusive Multiple Models for Predicting Suspended Sediment Loads," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4313-4342, September.

    More about this item

    Keywords

    Suspended sediment concentration; M5 model tree; MODIS; Global sensitivity;
    All these keywords.

    JEL classification:

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

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