Estimation of Suspended Sediment Concentration Using Remote Sensing Technique and M5 Model Tree

Authors

1 Department of Water Engineering, Agricultural Sciences and Natural Resources University of Khuzestan,

2 Graduate of Water Structures, Department of Water Engineering, Agricultural Sciences and Natural Resources University of Khuzestan, Iran

Abstract

Estimation of suspended sediment concentration (SSC) is one of the most important issues of river engineering, which can be used as an indicator of land use change, water quality studies and all projects related to constructions in the rivers. In this research, M5 model tree and the Moderate Resolution Imaging Spectroradiometer (MODIS) data have been used 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 obtained for days corresponding to field measurements of SSC for 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. Three regression equations have been developed by M5 model tree to estimate SSC at Ahvaz station, which can be employed in different conditions of discharge and NIR/R ratio. The results of statistical analysis illustrates that the M5 model outperforms the sediment rating curve (SRC) method, which is the most common method of estimating suspended sediment load. Nash-Sutcliffe efficiency index for the M5 model tree of 0.58 has been achieved which was much better than that of SRC method (0.24). At high fluxes, the efficiency of the SRC method is significantly reduced, while the model tree provides acceptable results. Global sensitivity analysis on M5 model showed that, 93% of output variance just determined 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.

Keywords

References

امیدوار، ج. و س. نوری.1393. راهنمای کاربردی نرم­افزار­های سنجش از دور. انتشارات صحرا، ص 275.
دستورانی، م.، خ. عظیمی فشی، ع. طالبی و م. اختصاصی. 1391. برآورد رسوب معلق با استفاده از شبکه عصبی مصنوعی (مطالعه موردی: حوضه آبخیز جامیشان استان کرمانشاه). پژوهشنامه مدیریت حوضه آبخیز، شماره 6، ص74-61.
شفاعی بجستانی، م. 1387. هیدرولیک رسوب. انتشارات دانشگاه چمران، ص 201.
ظهیری، ج. 1394. کاربرد مدل‏های ناپارامتریک CART و M5 در محاسبه عمق آبشستگی اطراف پایه‏های پل. فصلنامه علمی پژوهشی مهندسی آبیاری و آب ، شماره 20، ص 50-35.
Cai, L., D. Tang and C. Li. 2015. An investigation of spatial variation of suspended sediment concentration induced by a bay bridge based on Landsat TM and OLI data. Advances in Space Research, 56: 293-303.
Gordon, H.R. and A.Y. Morel. 1983. Remote assessment of ocean color for interpretation of satellite visible imagery: a review. Springer, New York.
Li, Y. and X. Li. 2016. Remote sensing observations and numerical studies of a super typhoon-induced suspended sediment concentration variation in the East China Sea. Ocean Modelling, 104: 187-202.
Min, J.E., J.H. Ryu, S. Lee and S. Son. 2012. Monitoring of suspended sediment variation using Landsat and MODIS in the Saemangeum coastal area of Korea. Marine Pollution Bulletin, 64: 382-390.
Moridnejad, A., H. Abdollahi, S.K. Alavipanah, J.M.V. Samani, O. Moridnejad and N. Karimi. 2015. Applying artificial neural networks to estimate suspended sediment concentrations along the southern coast of the Caspian Sea using MODIS images. Arabian Journal of Geosciences, 8: 891-901.
Park, E. and E.M. Latrubesse. 2014. Modeling suspended sediment distribution patterns of the Amazon River using MODIS data. Remote Sensing of Environment, 147: 232-242.
Quinlan, J.R. 1992. Learning with continuous classes. 5th Australian joint conference on artificial intelligence, 92: 343-348.
Robert, E., M. Grippa, L. Kergoat, S. Pinet, L. Gal, G. Cochonneau and J.M. Martinez. 2016. Monitoring water turbidity and surface suspended sediment concentration of the Bagre Reservoir (Burkina Faso) using MODIS and field reflectance data. International Journal of Applied Earth Observation and Geoinformation, 52: 243-251.
Saltelli, A., M. Ratto, T. Andres, F. Campolongo, J. Cariboni, D. Gatelli, M. Saisana and S. Tarantola. 2008. Global sensitivity analysis: the primer. John Wiley & Sons, USA.
Wang, J.J. and X.X. Lu. 2010. Estimation of suspended sediment concentrations using Terra MODIS: An example from the Lower Yangtze River, China. Science of the Total Environment, 408: 1131-1138.
Wang, Y. and I.H. Witten. 1996. Induction of model trees for predicting continuous classes.
Wu, B., Z. Wang, Q. Zhang, N. Shen and J. Liu. 2017. Modelling sheet erosion on steep slopes in the loess region of China. Journal of Hydrology, 553: 549-558.
Yang, C. T. 1996. Sediment transport: theory and practice. McGraw-Hill, New York.
Irrigation and Water Engineering
Volume 12, Issue 1 - Serial Number 45
October 2021
Pages 138-150

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