Advertisement

Water Resources Management

, Volume 33, Issue 7, pp 2335–2356 | Cite as

New Approach for Sediment Yield Forecasting with a Two-Phase Feedforward Neuron Network-Particle Swarm Optimization Model Integrated with the Gravitational Search Algorithm

  • Sarita Gajbhiye MeshramEmail author
  • M. A. Ghorbani
  • Ravinesh C. Deo
  • Mahsa Hasanpour Kashani
  • Chandrashekhar Meshram
  • Vahid Karimi
Article
  • 86 Downloads

Abstract

Predicting sediment yield is an important task for decision-makers in environmental monitoring and water management since the benefits of applying non-linear, artificial intelligence (AI) models for optimal prediction can be far reaching in real-life decision support systems. AI-based models are considered to be favorable predictive tools since the nonlinear nature of suspended sediment data series warrants the utilization of nonlinear predictive methods for feature extraction, and for accurate simulation of suspended sediment load. In this study, Artificial Neural Network (ANN) approaches are employed to estimate the monthly sediment load where the two-phase Feed-forward Neuron Network Particle Swarm Optimization Gravitational Search Algorithm (FNN-PSOGSA) is developed, and then evaluated in respect to 3 distinct algorithms: the Adaptive Neuro-Fuzzy Inference System (ANFIS), Feed-forward Neuron Network (FNN) and the single-phase Feed-forward Neuron Network Particle Swarm Optimization (FNN-PSO). The study is carried out using the monthly rainfall, runoff and sediment data spanning a 10 year period (2000–2009) where about 75% of data are used in model training phase, 25% in testing phase. Three statistical performance criteria namely: the mean absolute error (MAE), Nash-Sutcliffe coefficient (NSE) and the Willmott’s Index (WI) and diagnostic plots visualizing the tested results are used to evaluate the performance of four AI-based models. The results reveal that the objective model (the two-phase FNN-PSOGSA model) and the single-phase FNN-PSO model yielded more precise results compared to the other forecast models. This result accorded to an NSE value of 0.612 (for the FNN-PSOGSA model) vs. an NS value of 0.500, 0.331 and 0.244 for the FNN-PSO, FNN and ANFIS models, and WI = 0.832 vs. 0.771, 0.692 and 0.726, respectively The study also demonstrated that the FNN model generated slightly better results than the ANFIS model for the estimation of sediment load data but overall, the two-phase FNN-PSOGSA model outperformed all comparison models. In light of the superior performance, this research advocates that the fully-optimized two-phase FNN-PSOGSA model can be explored as a decision-support tool for monthly sediment load forecasting using the rainfall and runoff values as the predictor datasets.

Keywords

Neural networks PSO algorithm GSA algorithm Sediment load Modelling 

Notes

Acknowledgements

The authors are grateful to Central Water Commission (CWC) Bhopal, India for supplying the runoff and sediment data and Indian Metrological Department (IMD) Pune, India for supplying the rainfall data. Dr. R C Deo thanks the support of USQ s-ADOSP (2017) research program.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Asadi S, Shahrabi J, Abbaszadeh P, Tabanmehr S (2013) A new hybrid artificial neural networks for rainfall–runoff process modeling. Neurocomputing 121:470–480CrossRefGoogle Scholar
  2. Aytek A, Asce M, Alp M (2008) An application of artificial intelligence for rainfall–runoff modeling. J Earth Syst Sci 117(2):145–155CrossRefGoogle Scholar
  3. Behrang M, Assareh E, Ghalambaz M, Assari M, Noghrehabadi A (2011) Forecasting future oil demand in Iran using GSA (gravitational search algorithm). Energy 36:5649–5654CrossRefGoogle Scholar
  4. Besaw LE, Rizzo DM, Bierman PR, Hackett WR (2010) Advances in ungauged streamflow prediction using artificial neural networks. J Hydrol 386:27–37CrossRefGoogle Scholar
  5. Chai T, Draxler RRm (2014) Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature; Geosci. Model Dev 7:1247–1250CrossRefGoogle Scholar
  6. Chang FJ, Chang LC, Huang HL (2002) Real-time recurrent learning neural network for stream-flow forecasting. Hydrol Process 16(13):2577–2588CrossRefGoogle Scholar
  7. Coulibaly P, Anctil F, Bobée B (2000) Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J Hydrol 230(3–4):244–257CrossRefGoogle Scholar
  8. Diamantopoulou MJ, Papamichail DM, Antonopoulos VZ (2005) The use of a neural network technique for the prediction of water quality parameters. Oper Res 5(1):115–125Google Scholar
  9. Eslamian SS, Gohari SA, Zareian MJ, Firoozfar A (2012) Estimating penman–Monteith reference evapotranspiration using artificial neural networks and genetic algorithm: a case study. Arab J Sci Eng 37(4):935–944CrossRefGoogle Scholar
  10. Gaur S, Ch S, Graillot D, Chahar BR, Kumar DN (2013) Application of artificial neural networks and particle swarm optimization for the management of groundwater resources. Water Resour Manag 27:927–941CrossRefGoogle Scholar
  11. Ghorbani MA, Deo RC, Karimi V, Kashani MH, Ghorbani S (2019) Design and implementation of a hybrid MLP-GSA model with multi-layer perceptron-gravitational search algorithm for monthly lake water level forecasting. Stoch Env Res Risk A 33(1):125–147.  https://doi.org/10.1007/s00477-018-1630-1 CrossRefGoogle Scholar
  12. Houria B, Mahdi K, Zohra TF (2014) PSO-ANNs based suspended sediment concentration in Ksob basin, Algeria. J Eng Technol Res 6:129–136Google Scholar
  13. Huo Z, Feng S, Kang S, Dai X (2012) Artificial neural network models for reference evapotranspiration in an arid area of Northwest China. J Arid Environ 82:81–90CrossRefGoogle Scholar
  14. Jain SK, Das D, Srivastava DK (1999) Application of ANN for reservoir inflow prediction and operation. J Water Resour Planning Mgmt ASCE 125(5):263–271CrossRefGoogle Scholar
  15. Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685CrossRefGoogle Scholar
  16. Juan C, Genxu W, Tianxu M, Xiangyang S (2017) ANN model-based simulation of the runoff variation in response to climate change on the Qinghai-Tibet plateau, China. Advances in meteorology, volume 2017 article ID 9451802, 13 pagesGoogle Scholar
  17. Khalil B, Ouarda TBMJ, St-Hilaire A (2011) Estimation of water quality characteristics at ungauged sites using artificial neural networks and canonical correlation analysis. J Hydrol 405:277–287CrossRefGoogle Scholar
  18. Lal R (2001) Soil degradation by erosion. Land Degrad Dev 12(6):519–539CrossRefGoogle Scholar
  19. Lu WZ, Xue Y (2014) Prediction of particulate matter at street level using artificial neural networks coupling with chaotic particle swarm optimization algorithm. Build Environ 78:111–117CrossRefGoogle Scholar
  20. Maier HR, Dandy GC (1996) The use of artificial neural networks for the prediction of water quality parameters. Water Resour Res 32(4):1013–1022CrossRefGoogle Scholar
  21. Marzband M, Ghadimi M, Sumper A, Domínguez-García JL (2014) Experimental validation of a real-time energy management system using multi-period gravitational search algorithm for microgrids in islanded mode. Appl Energy 128:164–174CrossRefGoogle Scholar
  22. Mehr AD, Kahya E, Şahin A, Nazemosadat MJ (2015) Successive-station monthly streamflow prediction using different artificial neural network algorithms. Int J Environ Sci Technol 12(7):2191–2200CrossRefGoogle Scholar
  23. Melesse AM, Ahmad S, McClain ME, Wang X, Lim YH (2011) Suspended sediment load prediction of river systems: an artificial neural network approach. Agric Water Manag 98:855–866CrossRefGoogle Scholar
  24. Meshram SG, Ghorbani MA, Shamshirband S, Karimi V, Meshram C (2018) River flow prediction using hybrid PSOGSA algorithm based on feed-forward neural network. Soft Computing.  https://doi.org/10.1007/s00500-018-3598-7
  25. Mirjalili S, Hashim SZM, Sardroudi HM (2012) Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm. Appl Math Comput 218:11125–11137Google Scholar
  26. Moon S, Kang B (2016) Terrestrial sediment yield projection under the bias-corrected nonstationary scenarios with hydrologic extremes. Water 8:433–455CrossRefGoogle Scholar
  27. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models, part I - a discussion of principles. J Hydrol 10:282–290CrossRefGoogle Scholar
  28. Papa JP et al. (2011) Feature selection through gravitational search algorithm. Acoustics, speech and signal processing (ICASSP), 2011 IEEE international conference on, IEEE: 2052–2055Google Scholar
  29. Qasem SN, Ebtehaj I, Bonakdari H (2017) Potential of radial basis function network with particle swarm optimization for prediction of sediment transport at the limit of deposition in a clean pipe Sustainable, Water Resources Management: 1–11Google Scholar
  30. Radosavljević J, Klimenta D, Jevtić M, Arsić N (2015) Optimal power flow using a hybrid optimization algorithm of particle swarm optimization and gravitational search algorithm. Electric Power Components Syst 43:1958–1970CrossRefGoogle Scholar
  31. Rajurkar MP, Kothyari UC, Chaube UC (2002) Artificial neural networks for daily rainfall—runoff modelling. Hydrol Sci J 47(6):865–877CrossRefGoogle Scholar
  32. Rashedi E, Pour HN, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179:2232–2248CrossRefGoogle Scholar
  33. Saad M, Bigras P, Turgeon A, Duquette R (1996) Fuzzy learning decomposition for the scheduling of hydroelectric power systems. Water Resour Res 32(1):179–186CrossRefGoogle Scholar
  34. Sattari MT, Yurekli K, Pal M (2012) Performance evaluation of artificial neural network approaches in forecasting reservoir inflow. Appl Math Model 36(6):2649–2657CrossRefGoogle Scholar
  35. Tayebiyan A, Mohammad TA, Ghazali AH, Mashohor S (2016) Artificial neural network for modelling rainfall-runoff. Pertanika J Sci Technol 24(2):319–330Google Scholar
  36. Xiong Y, Luo Y, Wang Y, Traore S, Xu J, Jiao X, Fipps G (2016) Forecasting daily reference evapotranspiration using the Blaney–Criddle model and temperature forecasts. J Arch Agro Soil Science 62(6):790–805CrossRefGoogle Scholar
  37. Yadav A, Chatterjee S, Equeenuddin SM (2017) Prediction of suspended sediment yield by artificial neural network and traditional mathematical model in Mahanadi river basin, India. doi: https://doi.org/10.1007/s40899-017-0160-1
  38. Zealand CM, Burn DH, Simonovic SP (1999) Short term streamflow forecasting using artificial neural networks. J Hydrol 214(1–4):32–48CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Sarita Gajbhiye Meshram
    • 1
    Email author
  • M. A. Ghorbani
    • 2
    • 3
  • Ravinesh C. Deo
    • 4
  • Mahsa Hasanpour Kashani
    • 5
  • Chandrashekhar Meshram
    • 1
  • Vahid Karimi
    • 2
  1. 1.Department of Mathematics & Computer ScienceRani Durgawati UniversityJabalpurIndia
  2. 2.Department of Water EngineeringUniversity of TabrizTabrizIran
  3. 3.Engineering FacultyNear East UniversityMersinTurkey
  4. 4.School of Agricultural, Computational and Environmental Sciences, Institute of Agriculture and EnvironmentUniversity of Southern QueenslandSpringfieldAustralia
  5. 5.Department of Water EngineeringUniversity of Mohaghegh ArdabiliArdabilIran

Personalised recommendations