Advertisement

Evaluating Different Machine Learning Models for Runoff and Suspended Sediment Simulation

  • Ashish Kumar
  • Pravendra Kumar
  • Vijay Kumar SinghEmail author
Article
  • 27 Downloads

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.

Keywords

ANN ANFIS Sensitivity analysis Gamma test 

Notes

Compliance with Ethical Standards

Conflict of Interest

None.

References

  1. Abbasi Shoshtari S, Kashefipoor M (2006) Estimation of suspended sediment using artificial neural networks (Case study: Ahwaz station) 7th international river engineering conference, Ahwaz, IR IRAN. p 813Google Scholar
  2. Adamowski J, Chan HF (2011) A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 407:28–40CrossRefGoogle Scholar
  3. 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 21:447.  https://doi.org/10.1007/s12205-016-0444-2 CrossRefGoogle Scholar
  4. Afan HA, El-Shafie A, Yaseen ZM et al (2015) ANN based sediment prediction model utilizing different input scenarios. Water Resour Manag.  https://doi.org/10.1007/s11269-014-0870-1
  5. Akrami SA, Nourani V, Hakim S (2014) Development of nonlinear model based on wavelet-ANFIS for rainfall forecasting at Klang Gates Dam. Water Resour Manag 28:2999–3018CrossRefGoogle Scholar
  6. Alizadeh MJ, Kavianpou MR, Kisi O, Nourani V (2017) A new approach for simulating and forecasting the rainfall-runoff process within the next two months. J Hydrol 548:588–597.  https://doi.org/10.1016/j.jhydrol.2017.03.032 CrossRefGoogle Scholar
  7. Alp M, Cigizoglu HK (2007) Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data. Environ Model Softw.  https://doi.org/10.1016/j.envsoft.2005.09.009
  8. Buyukyildiz M, Kumcu SY (2017) An estimation of the suspended sediment load using adaptive network based fuzzy inference system, support vector machine and artificial neural network models. Water Resour Manag 31:1343.  https://doi.org/10.1007/s11269-017-1581-1 CrossRefGoogle Scholar
  9. Chang FJ, Chen PA, Lu YR, Huang E, Chang KY (2014a) Real-time multistep- ahead water level forecasting by recurrent neural networks for urban flood control. J Hydrol 517:836–846CrossRefGoogle Scholar
  10. Chang FJ, Chen PA, Lu YR, Huang E, Chang KY (2014b) Watershed rainfall forecasting using neuro-fuzzy networks with the assimilation of multi-sensor information. J Hydrol 508:374–384CrossRefGoogle Scholar
  11. Ebtehaj I, Bonakdari H (2014) Performance evaluation of adaptive neural fuzzy inference system for sediment transport in sewers. Water Resour Manag.  https://doi.org/10.1007/s11269-014-0774-0
  12. Gholami V, Khaleghi MR, Sebghati (2016) A method of groundwater quality assessment based on fuzzy network-CANFIS and geographic information system (GIS). Appl Water Sci.  https://doi.org/10.1007/s13201-016-0508-y
  13. Jain SK (2012) Modeling river stage–discharge–sediment rating relation using support vector regression. Hydrol Res.  https://doi.org/10.2166/nh.2011.101
  14. Jang J-SR (1997) Adaptive network-based fuzzy inference system (ANFIS). IEEE Trans Syst Man Cybern 23:665–685CrossRefGoogle Scholar
  15. Jothiprakash V, Magar RB (2012) Multi-time-step ahead daily and hourly intermittent reservoir inflow predictionby artificial intelligent techniques using lumped and distributed data. J Hydrol 450-451:293–307CrossRefGoogle Scholar
  16. Kaltech AM (2015) Wavelet genetic algorithm-support vector regression (wavelet GA-SVR) for monthly flow forecasting. Water Resour Manag 29(4):1283–1293CrossRefGoogle Scholar
  17. Kisi O (2010) Wavelet regression model for short-term streamflow forecasting. J Hydrol 389:344–353CrossRefGoogle Scholar
  18. Kisi O, Cimen M (2011) A wavelet-support vector machine conjunction model for monthly streamflow forecasting. J Hydrol 399:132–140CrossRefGoogle Scholar
  19. Kisi O, Karmani ZM (2016) Suspended sediment modeling using neuro-fuzzy embedded fuzzy c-means clustering technique. Water Resour Manag.  https://doi.org/10.1007/s11269-016-1405-8
  20. Kisi O, Haktanir T, Ardiclioglu M, Ozturk O, Yalcin E, Uludag S (2009) Adaptive neuro-fuzzy computing technique for suspended sedimentestimation. Adv Eng Softw 40:438–444CrossRefGoogle Scholar
  21. Lohani AK, Goel N, Bhatia K (2014) Improving real time flood forecasting using fuzzy inference system. J Hydrol 509:25–41CrossRefGoogle Scholar
  22. Loukas YL (2001) Adaptive neuro-fuzzy inference system: an instant and architecture-free predictor for improved QSAR studies. J Med Chem 44(17):2772–2783CrossRefGoogle Scholar
  23. McCulloch W, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(5):115–133CrossRefGoogle Scholar
  24. Monfared A (2016) Simulation of suspended sediment load of Shapour River with using of artificial nerve network patterns (ANN) and phasic nerve (ANFIS) schedule series (stochastic). Int J Appl Eng Res 11(5):3645–3650Google Scholar
  25. Noori R, Karbassi AR, Moghaddamnia A, Han D, Zokaei-Ashtiani MH, Farokhnia A, GhafariGousheh M (2011) Assessment of input variables determination on the SVM model performance using PCA, gamma test and forward selection techniques for monthly streamflow prediction. J Hydrol 401(3–4):177–189CrossRefGoogle Scholar
  26. Pahlavani, Dehghani AA, Bahremand AR (2017) Intelligent estimation of flood hydrographs using an adaptive neuro–fuzzy inference system (ANFIS). Earth Syst Environ 3:35.  https://doi.org/10.1007/s40808-017-0305-0 CrossRefGoogle Scholar
  27. 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
  28. Ramezani F, Nikoo M, Nikoo M (2014) Artificial neural network weights optimization based on social-based algorithm to realize sediment over the river. Soft Comput 19(2):375–387.  https://doi.org/10.1007/s00500-014-1258-0 CrossRefGoogle Scholar
  29. Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65(6):386–408CrossRefGoogle Scholar
  30. Rumelheart DE, Hinton GE, Williams RJ (1986) Learning internal representation by error-propagation. In: Rumelhart DE, McClelland JL (eds) Parallel distribution processing: explorations in the microstructure of cognition. p 54–164Google Scholar
  31. Seo Y, Kim S, Kisi O, Singh VP (2015) Daily water level forecasting using wavelet decomposition and artificial intelligence techniques. J Hydrol 520:224–243CrossRefGoogle Scholar
  32. Singh VK, Kumar P, Singh BP, Malik A (2016a) A comparative study of adaptive neuro fuzzy inference system (ANFIS) and multiple linear regression (MLR) for rainfall-runoff modellin. Int J Sci Natur 7(4):714–723Google Scholar
  33. Singh VK, Kumar P, Singh BP (2016b) Rainfall-runoff modeling using artificial neural networks (ANNs) and multiple linear regression (MLR) techniques. Ind J Eco 43(2):436–442Google Scholar
  34. Singh VK, Singh BP, Kisi O, Kushwaha DP (2018a) Spatial and multi-depth temporal soil temperature assessment by assimilating satellite imagery, artificial intelligence and regression-based models in arid area. Comput Electron Agric.  https://doi.org/10.1016/j.compag.2018.04.019
  35. Singh VK, Kumar D, Kashyap PK, Kisi O (2018b) Simulation of suspended sediment based on gamma test, heuristic, and regression-based techniques. Environ Earth Sci.  https://doi.org/10.1007/s12665-018-7892-6
  36. Sudheer KP, Gosain AK, Ramasastri KS (2002) A data-driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrol Process 16(6):1325–1330CrossRefGoogle Scholar
  37. Sudheer C, Maheswaran R, Panigrahi BK, Mathur S (2014) A hybrid SVM-PSO model for forecasting monthly streamflow. Neural Comput Applic 24:1381–1389CrossRefGoogle Scholar
  38. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its application to modeling and control. IEEE Trans Syst Man Cybern 15:116–1332CrossRefGoogle Scholar
  39. Wei S, Song J, Khan NI (2012) Simulating and predicting river discharge time series using a wavelet-neural network hybrid modelling approach. Hydrol Process 26(2):281–296CrossRefGoogle Scholar
  40. Yang CT, Marsooli R, Aalami MT (2009) Evaluation of total load sedimenttransport formulas using ANN. Int J Sedim Res 24:274–286CrossRefGoogle Scholar
  41. Yaseen ZM et al (2016) RBFNN versus FFNN for daily river flow forecasting at Johor River, Malaysia. Neural Comput Applic 27(6):1533–1542CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Soil and Water Conservation EngineeringG.B. Pant University of Agriculture and TechnologyPantnagarIndia

Personalised recommendations