Environmental Science and Pollution Research

, Volume 25, Issue 35, pp 35693–35706 | Cite as

Modeling daily suspended sediment load using improved support vector machine model and genetic algorithm

  • Mitra Rahgoshay
  • Sadat Feiznia
  • Mehran Arian
  • Seyed Ali Asghar Hashemi
Research Article


Prediction of sediment volume and sediment load is always one of the important issues for decision-makers of watershed basins. The present study investigated the daily suspended sediment load in a watershed basin using the improved support vector machine method. Since in most of the previous studies, the coefficients of the support vector machine method had been calculated based on trial and error, in the present study, the combination of the support vector machine and the genetic algorithm is used. In the first step, the unknown parameters of the support vector machine are calculated and then, the sediment load simulation is performed. Two case studies in the present work involve two earth dams in Semnan Province called Veynakeh and Royan. Furthermore, multivariate adaptive regression spline (MARS) and MT tree model (M5T) methods are used for comparison. The results indicated that the input combination of discharge data at the current time and one, two, and three previous days has the best performance for all models. Also, the support vector machine-genetic algorithm (SVM-GA) model has a lower root mean square error (RMSE) and mean absolute error (MAE) compared to the MARS and M5T models for both stations. In addition, comparing observational data with simulation data based on the R2 coefficient suggested that the SVM-GA model offers more accurate results than the other two methods. Accordingly, the SVM-GA method used in this study has a high potential for simulating sediment volume.


Sediment Genetic algorithm Support vector machine Tree model 


Author contribution

A new method for the support vector machine was used to simulate the sediment load. All the authors contributed to the manuscript development by writing, discussing changes for clarity, and technical usefulness and correction.

Supplementary material

11356_2018_3533_MOESM1_ESM.docx (14 kb)
ESM 1 (DOCX 14 kb)


  1. Adarsh S, Reddy MJ (2018) Multiscale modelling of daily suspended sediment load using MEMD-SLR Coupled approach. In: Handbook of research on predictive modeling and optimization methods in science and engineering. IGI Global, pp 264–275Google Scholar
  2. Adib A, Mahmoodi A (2017) Prediction of suspended sediment load using ANN GA conjunction model with Markov chain approach at flood conditions. KSCE J Civ Eng 21(1):447–457CrossRefGoogle Scholar
  3. Afan HA, El-Shafie A, Yaseen ZM, Hameed MM, Mohtar WHMW, Hussain A (2015) ANN based sediment prediction model utilizing different input scenarios. Water Resour Manag 29(4):1231–1245CrossRefGoogle Scholar
  4. Ahilan S, Guan M, Sleigh A, Wright N, Chang H (2018) The influence of floodplain restoration on flow and sediment dynamics in an urban river. J Flood Risk Manage 11:S986–S1001CrossRefGoogle Scholar
  5. Bharti B, Pandey A, Tripathi SK, Kumar D (2017) Modelling of runoff and sediment yield using ANN, LS-SVR, REPTree and M5 models. Hydrol Res 48(6):1489–1507CrossRefGoogle Scholar
  6. Choubin B, Darabi H, Rahmati O, Sajedi-Hosseini F, Kløve B (2018) River suspended sediment modelling using the CART model: a comparative study of machine learning techniques. Sci Total Environ 615:272–281CrossRefGoogle Scholar
  7. Deo RC, Kisi O, Singh VP (2017) Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model. Atmos Res 184:149–175CrossRefGoogle Scholar
  8. Gholami V, Booij MJ, Tehrani EN, Hadian MA (2018) Spatial soil erosion estimation using an artificial neural network (ANN) and field plot data. Catena 163:210–218CrossRefGoogle Scholar
  9. Gil JM, Montes JFA, Alba E, Aldana-Montes JF (2018) Optimizing ontology alignments by using genetic algorithmsGoogle Scholar
  10. Golkarian A, Naghibi SA, Kalantar B, Pradhan B (2018) Groundwater potential mapping using C5. 0, random forest, and multivariate adaptive regression spline models in GIS. Environ Monit Assess 190(3):149CrossRefGoogle Scholar
  11. Hatten JA, Segura C, Bladon KD, Hale VC, Ice GG, & Stednick JD (2018) Effects of contemporary forest harvesting on suspended sediment in the Oregon Coast Range: Alsea Watershed Study Revisited. Forest Ecology and Management 408:238–248CrossRefGoogle Scholar
  12. Heddam S, Kisi O (2018) Modelling daily dissolved oxygen concentration using least square support vector machine, multivariate adaptive regression splines and M5 model tree. J Hydrol 559:499–509CrossRefGoogle Scholar
  13. Kisi O (2012) Modeling discharge-suspended sediment relationship using least square support vector machine. J Hydrol 456:110–120CrossRefGoogle Scholar
  14. Kisi O, Shiri J (2012) River suspended sediment estimation by climatic variables implication: comparative study among soft computing techniques. Comput Geosci 43:73–82CrossRefGoogle Scholar
  15. Kisi O, Parmar KS, Soni K, Demir V (2017) Modeling of air pollutants using least square support vector regression, multivariate adaptive regression spline, and M5 model tree models. Air Qual Atmos Health 10(7):873–883CrossRefGoogle Scholar
  16. Kumar D, Pandey A, Sharma N, & Flügel WA (2016) Daily suspended sediment simulation using machine learning approach. Catena 138:77–90CrossRefGoogle Scholar
  17. Kumar R, Kumar R, Singh S, Singh A, Bhardwaj A, Kumari A, Saha A (2018) Dynamics of suspended sediment load with respect to summer discharge and temperatures in Shaune Garang glacierized catchment, Western Himalaya. Acta Geophys:1–12Google Scholar
  18. Lang Z, Li Y, Hu Y, Li B, & Wang J (2018) A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework. Theoretical and Applied Climatology 1–13Google Scholar
  19. Liang Z, Li Y, Hu Y, Li B, Wang J (2017) A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework. Theor Appl Climatol:1–13Google Scholar
  20. Lin S, Qi J, Jones JR, Stevenson RJ (2018) Effects of sediments and coloured dissolved organic matter on remote sensing of chlorophyll-a using Landsat TM/ETM+ over turbid waters. Int J Remote Sens 39(5):1421–1440CrossRefGoogle Scholar
  21. Liu QJ, Shi ZH, Fang NF, Zhu HD, Ai L (2013) Modeling the daily suspended sediment concentration in a hyperconcentrated river on the Loess Plateau, China, using the wavelet–ANN approach. Geomorphology 186:181–190CrossRefGoogle Scholar
  22. Liu CG, Li ZY, Hao Y, Xia J, Bai FW, Mehmood MA (2018) Computer simulation elucidates yeast flocculation and sedimentation for efficient industrial fermentation. Biotechnol J 13Google Scholar
  23. Malik A, Kumar A, Piri J (2017) Daily suspended sediment concentration simulation using hydrological data of Pranhita River Basin, India. Comput Electron Agric 138:20–28CrossRefGoogle Scholar
  24. Moeeni H, Bonakdari H (2018) Impact of normalization and input on ARMAX-ANN model performance in suspended sediment load prediction. Water Resour Manag:1–19Google Scholar
  25. Mousavi-Avval SH, Rafiee S, Sharifi M, Hosseinpour S, Notarnicola B, Tassielli G, Renzulli PA (2017) Application of multi-objective genetic algorithms for optimization of energy, economics and environmental life cycle assessment in oilseed production. J Clean Prod 140:804–815CrossRefGoogle Scholar
  26. Negm A, Elsahabi M, Abdel-Nasser M, Mahmoud K, Ali K (2018) Impacts of GERD on the accumulated sediment in Lake Nubia using machine learning and GIS techniquesCrossRefGoogle Scholar
  27. Nourani V, Kalantari O, Baghanam AH (2012) Two semidistributed ANN-based models for estimation of suspended sediment load. J Hydrol Eng 17(12):1368–1380CrossRefGoogle Scholar
  28. Nourani V, Alizadeh F, Roushangar K (2016) Evaluation of a two-stage SVM and spatial statistics methods for modeling monthly river suspended sediment load. Water Resour Manag 30(1):393–407CrossRefGoogle Scholar
  29. Rajaee T, Nourani V, Zounemat-Kermani M, Kisi O (2010) River suspended sediment load prediction: application of ANN and wavelet conjunction model. J Hydrol Eng 16(8):613–627CrossRefGoogle Scholar
  30. Roushangar K, & Ghasempour R (2017) Estimation of bedload discharge in sewer pipes with different boundary conditions using an evolutionary algorithm. International Journal of Sediment Research, 32(4):564–574CrossRefGoogle Scholar
  31. Sahraei S, Alizadeh MR, Talebbeydokhti N, Dehghani M (2018) Bed material load estimation in channels using machine learning and meta-heuristic methods. J Hydroinf 20(1):100–116CrossRefGoogle Scholar
  32. Singh A, Imtiyaz M, Isaac RK, Denis DM (2014) Assessing the performance and uncertainty analysis of the SWAT and RBNN models for simulation of sediment yield in the Nagwa watershed, India. Hydrol Sci J 59(2):351–364CrossRefGoogle Scholar
  33. Skardi MJE, Afshar A, Saadatpour M, Solis SS (2015) Hybrid ACO–ANN-based multi-objective simulation–optimization model for pollutant load control at basin scale. Environ Model Assess 20(1):29–39CrossRefGoogle Scholar
  34. Talebi A, Mahjoobi J, Dastorani MT, Moosavi V (2017) Estimation of suspended sediment load using regression trees and model trees approaches (case study: Hyderabad drainage basin in Iran). ISH J Hydraul Eng 23(2):212–219CrossRefGoogle Scholar
  35. Wu L, Peng M, Qiao S, Ma XY (2018) Effects of rainfall intensity and slope gradient on runoff and sediment yield characteristics of bare loess soil. Environ Sci Pollut Res 25(4):3480–3487CrossRefGoogle Scholar
  36. Yilmaz B, Aras E, Nacar S, Kankal M (2018) Estimating suspended sediment load with multivariate adaptive regression spline, teaching-learning based optimization, and artificial bee colony models. Sci Total Environ 639:826–840CrossRefGoogle Scholar
  37. Zamani B, Koch M, Hodges BR, Fakheri-Fard A (2018) Pre-impoundment assessment of the limnological processes and eutrophication in a reservoir using three-dimensional modeling: Abolabbas reservoir, Iran. J Appl Water Eng Res 6(1):48–61CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Mitra Rahgoshay
    • 1
  • Sadat Feiznia
    • 2
  • Mehran Arian
    • 3
  • Seyed Ali Asghar Hashemi
    • 4
  1. 1.Department of Earth Sciences, Science and Research BranchIslamic Azad UniversityTehranIran
  2. 2.Faculty of Natural ResourcesUniversity of TehranKarajIran
  3. 3.Department of Earth Sciences, Science and Research BranchIslamic Azad UniversityTehranIran
  4. 4.Department of Watershed ManagementAgricultural and Natural Resources Research and Education CenterSemnanIran

Personalised recommendations