Skip to main content
SpringerLink
Log in

A Multi-Layer Perceptron (MLP)-Fire Fly Algorithm (FFA)-based model for sediment prediction

  • Application of soft computing
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

The prediction of river sediment load is an essential issue in water resource engineering problems. In this study, artificial neural network employed in order to estimate the daily sediment load on rivers. Two different algorithms, multi-layer perceptron (MLP) and hybrid MLP-FFA (MLP integrated with the FFA) were used for this purpose in the Lake Mahabad, Iran. For this purpose, nine different scenarios are considered as inputs of the models. Performance of selected models was evaluated on basis of performance criterion namely root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2) for choosing best fit model. The results indicated that the new hybrid model MLP-FFA is successful in estimating sediment load with high accuracy as compared with its alternatives with RMSE = 2018 ton/day, MAE = 1698 and R2 = 0.95, which were much lower than those of MLP-based model with RMSE = 3044 ton/day, MAE = 2481 and R2 = 0.90. The results of the present study confirmed the suitability of proposed methodology for precise modeling of suspended sediment load.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  • Adamowski J, Chan H, Prasher S, Sharda VN (2012) Comparison of multivariate adaptive regression splines with coupled wavelet transform artificial neural networks for runoff forecasting in Himalayan micro-watersheds with limited data. J Hydroinf 3:731–744

    Google Scholar 

  • Ahmad MM, Ghumman AR, Ahmad S (2009) Estimation of Clark’s instantaneous unit hydrograph parameters and development of direct surface runoff hydrograph. Water Resour Manag 23:2417–2435. https://doi.org/10.1007/s11269-008-9388-8

    Article  Google Scholar 

  • Ahmad S, Kalra A, Stephen H (2010) Estimating soil moisture using remote sensing data: a machine learning approach. Adv Water Resour 33(1):69–80

    Google Scholar 

  • Ahmadi M, Minaei M, Ebrahimi O (2020) Evaluation of WEPP and EPM for improved predictions of soil erosion in mountainous watersheds: a case study of Kangir River basin. Iran Model Earth Syst Environ 6:2303–2315. https://doi.org/10.1007/s40808-020-00814-w

    Article  Google Scholar 

  • Al-Shammari E, Mohammadi K, Keivani A, SitiHafizahAb Hamid, Akib S, Shamshirband S, Dalibor P (2016) Prediction of daily dew point temperature using a model combining the support vector machine with firefly algorithm, vol 5(no 142)

  • Ampomah R, Hosseiny H, Zhang L, Smith V, Sample-Lord K (2010) A regression-based prediction model of suspended sediment yield in the Cuyahoga River in Ohio using historical satellite images and precipitation data. Water 12:881

    Google Scholar 

  • Arekhi S, Niazi Y, Kalteh AM (2012) Soil erosion and sediment yield modeling using RS and GIS techniques: a case study. Iran Arab J Geosci 5:285–296. https://doi.org/10.1007/s12517-010-0220-4

    Article  Google Scholar 

  • Asres MT, Awulachew SB (2010) SWAT based runoff and sediment yield modelling: a case study of the Gumera watershed in the Blue Nile basin. Ecohydrol Hydrobiol 10(2–4):191–199

    Google Scholar 

  • Aytek A (2008) Genetic programming approach to suspended sediment modelling. J Hydrol 351:288–298

    Google Scholar 

  • Belayneh A, Adamowski J, Khalil B, Quilty J (2016) Coupling machine learning methods with wavelet transforms and the bootstrap and boosting ensemble approaches for drought prediction. Atmos Res 172–173:37–47

    Google Scholar 

  • Borrelli P, Robinson DA, Fleischer LR (2017) An assessment of the global impact of 21st century land use change on soil erosion. Nat Commun 8:2013. https://doi.org/10.1038/s41467-017-02142-7

    Article  Google Scholar 

  • Boukhrissa ZA, Khanchoul K, Bissonnais YL, Tourki M (2013) Prediction of sediment load by sediment rating curve and neural network (ANN) in El Kebir catchment, Algeria. J Earth Syst Sci 122(5):1303–1312

    Google Scholar 

  • Chandra P, Patel PL, Porey PD, Gupta ID (2014) Estimation of sediment yield using SWAT model for Upper Tapi basin for Upper Tapi basin. ISH J Hydraul Eng 20(3):1–11

    Google Scholar 

  • Cigizoglu HK (2004) Estimation and forecasting of daily suspended sediment data by multi-layer perceptron’s. Adv Water Resour 27:185–195

    Google Scholar 

  • Cobaner M, Unal B, Kisi O (2009) Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data. J Hydrol 367(1–2):52–61

    Google Scholar 

  • Coulibaly P, Anctil F, Aravena R, Bobee B (2001) Artificial neural network modeling of water table depth fluctuations. Water Resour Res 37(4):885–896

    Google Scholar 

  • Darabi H, Mohamadi S, Karimidastenaei Z, Kisi O, Ehteram M, ELShafie A, Haghighi AT (2021) Prediction of daily suspended sediment load (SSL) using new optimization algorithms and soft computing models. Soft Comput 25:7609–7626. https://doi.org/10.1007/s00500-021-05721-5

    Article  Google Scholar 

  • Donovan G, Butry D, Mao M (2016) Statistical Analysis of Vegetation and Storm water Runoff in an Urban Watershed during summer and Winter Storms in Portland, Oregon U.S. Arboric Urban For 42(5):318–328

    Google Scholar 

  • Dutta S (2015) Determination of reservoir capacity using linear programming. Int J Innov Res Sci Eng Technol 4(10):9549–9556

    Google Scholar 

  • Dutta S, Sen D (2018) Application of SWAT model for predicting soil erosion and sediment yield. Sustain Water Resour Manag 4:447–468. https://doi.org/10.1007/s40899-017-0127-2

    Article  Google Scholar 

  • Ettinger E, Mounaud L, Magill C, Yao-Lafourcade A-F, Thouret J, Manville V, Negulescu C, Zuccaro G, Gregorio D, Nardone S, Uchuchoque JAL, Arguedas A, Macedo L, Nélida ML (2016) Building vulnerability to hydro-geomorphic hazards: estimating damage probability from qualitative vulnerability assessment using logistic regression. J Hydrol 541:563–581

    Google Scholar 

  • Fahimi F, Yaseen ZM, El-shafie A (2017) Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review. Theor Appl Climatol 128:875–903. https://doi.org/10.1007/s00704-016-1735-8

    Article  Google Scholar 

  • Fang K, Siva Kumar B, Woldemeskel F (2017) Complex networks, community structure, and catchment classification in a large-scale river basin. J Hydrol 545:478–493

    Google Scholar 

  • Fateen S, Bonilla-Petriciolet A, Pandu Rangaiah G (2012) Evaluation of covariance matrix adaptation evolution strategy, shuffled complex evolution and firefly algorithms for phase stability. Phase Equilib Chem Equilib Probl 90(12):2051–2071

    Google Scholar 

  • Fu G, Chen S, McCool DK (2006) Modeling the impacts of no-till practice on soil erosion and sediment yield with RUSLE, SEDD, and ArcView GIS. Soil Till Res 85(1–2):38–49

    Google Scholar 

  • Ghorbani MA, Khatibi R, Hosseini B, Bilgili M (2013) Relative importance of parameters affecting wind speed prediction using artificial neural networks. Theor Appl Climatology 114(1):107–111

    Google Scholar 

  • Ghorbani MA, Shamshirband S, ZareHaghie D, Azania A, Bonakdarif H, Ebtehajf I (2017) Application of firefly algorithm-based support vector machines for prediction of field capacity and permanent wilting point. Soil Till Res 172:32–38

    Google Scholar 

  • Gocić M, Motamedi SH, Shamshirband SH, Petković D, Ch S, Hashim R, Arif M (2015) Soft computing approaches for forecasting reference evapotranspiration. Comput Electron Agric 113:164–173

    Google Scholar 

  • Govindaraju RS (2000) Artificial neural networks in hydrology. I: preliminary concepts. J Hydrol Eng 5(2):115–123

    Google Scholar 

  • Graves A, Ferńandez S, Gomez F, Schmidhuber J (2006) Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks, in ICML, Pittsburgh, USA

  • Heddam S (2016) Simultaneous modelling and forecasting of hourly dissolved oxygen concentration (DO) using radial basis function neural network (RBFNN) based approach: a case study from the Klamath River, Oregon, USA, and Model. Earth Syst Environ 2:135

    Google Scholar 

  • Hemalatha C, ValanRajkumar M, Vidhya Krishnan G (2016) Simulation and analysis of MPPT control with modified firefly algorithm for photovoltaic system. Int J Innov Stud Sci Eng Technol 2(11):48–52

    Google Scholar 

  • Hsu KL, Gupta HV, Sorooshian S (1995) Artificial neural network modeling of the rainfall-runoff process. Water Resour Res 31(10):2517–2530

    Google Scholar 

  • Iraji H, Mohammadi M, Shakouri B, Meshram SG (2020) Predicting Reservoirs Volume Reduction using Artificial Neural Network. Arab J Geosci 13:835. https://doi.org/10.1007/s12517-020-05772-2

    Article  Google Scholar 

  • Kayarvizhy N, Kanmani S, Uthariaraj RV (2014) ANN Models optimized using swarm intelligence algorithms. WSEAS Trans Comput 13:501–519

    Google Scholar 

  • Khan MYA, Tian F, Hasan F, Chakrapani GJ (2018) Artificial neural network simulation for prediction of suspended sediment concentration in the River Ramganga, Ganges Basin, India. Int J Sedim Res. https://doi.org/10.1016/j.ijsrc.2018.09.001

    Article  Google Scholar 

  • Kisi O (2007) Streamflow forecasting using different artificial neural network algorithms. J Hydrol Eng 12:532–539

    Google Scholar 

  • Kişi O (2009) Wavelet regression model as an alternative to neural networks for monthly streamflow forecasting. Hydrol Process 23(24):3583–3597

    Google Scholar 

  • Lafdani EK, Nia AM, Ahmadi A (2013) Daily suspended sediment load prediction using artificial neural networks and support vector machines. J Hydrol 478:50–62. https://doi.org/10.1016/j.jhydrol.2012.11.048

    Article  Google Scholar 

  • Liu Y, Jiang H (2019) Sediment yield modeling using SWAT model: case of Changjiang River Basin. IOP Conf Ser Earth Environ Sci 234:012031

    Google Scholar 

  • Liu QQ, Chen L, Li JC, Singh VP (2004) Two-dimensional kinematic wave model of overland-flow. J Hydrol 291(1–2):28–41

    Google Scholar 

  • Lohani AK, Goel NK, Bhatia KKS (2007) Deriving stage–discharge–sediment concentration relationships using fuzzy logic. Hydrol Sci J 52(4):793–807

    Google Scholar 

  • Lukasik S, Zak S (2009) Firefly algorithm for continuous constrained optimization tasks. ICCCI 2009, LNAI 5796. Springer, Berlin, pp 97–106s

  • Maier H, Jain A, Dandy G, Sudheer KP (2010) Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions. Environ Model Soft 25:891–909

    Google Scholar 

  • 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(5):855–866. https://doi.org/10.1016/j.agwat.2010.12.012

    Article  Google Scholar 

  • Meshram SG, Ghorbani MA, Shamshirband S, Karimi V, Meshram C (2019a) River flow prediction using hybrid PSOGSA algorithm based on feed-forward neural network. Soft Comput 23(20):10429–10438

    Google Scholar 

  • Meshram SG, Ghorbani MA, Deo RC, Kashani MH, Meshram C, Karimi V (2019b) New Approach for Sediment Yield Forecasting with a Two-Phase Feed forward Neuron Network-Particle Swarm Optimization Model Integrated with the Gravitational Search Algorithm. Water Resour Manag 33(7):2335–2356

    Google Scholar 

  • Meshram SG, Singh VP, Kisi O, Karimi V, Meshram C (2020) Application of Artificial Neural Networks, Support Vector Machine and Multiple Model- ANN to Sediment Yield Prediction. Water Resour Manag. https://doi.org/10.1007/s11269-020-02672-8

    Article  Google Scholar 

  • Meshram SG, Safari MJS, Khosravi K, Meshram C (2021a) Iterative classifier optimizer-based pace regression and random forest hybrid models for suspended sediment load prediction. Environ Sci Pollut Res 28(1):11637–11649

    Google Scholar 

  • Meshram SG, Pourghasemi HR, Abba SI, Alvandi E, Meshram C, Khedher KM (2021b) A comparative study between dynamic and soft computing models for sediment forecasting. Soft comput. https://doi.org/10.1007/s00500-021-05834-x

    Article  Google Scholar 

  • Meshram SG, Meshram C, Santos CAG, Benzougagh B, Khedher KM (2021c) Stream Flow Prediction Based on Artificial Intelligence Techniques. Iranian J Sci Technol, Trans Civil Eng. https://doi.org/10.1007/s40996-021-00696-7

    Article  Google Scholar 

  • Moazenzadeh R, Mohammadi B, Shamshirband S, Chau KW (2018) Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran. Eng Appl Comput Fluid Mech 12(1):584–597

    Google Scholar 

  • Mohammadi B, Guan Y, Moazenzadeh R, Safari MJS (2021) Implementation of hybrid particle swarm optimization-differential evolution algorithms coupled with multi-layer perceptron for suspended sediment load estimation. Catena 198:105024

    Google Scholar 

  • Nicklow JW, Mays LW (2000) Optimization of multiple reservoir networks for sedimentation control. J Hydraul Eng 126(4):232–242

    Google Scholar 

  • Nourani V, Mousavi SH, Dabrowska D, Sadikoglu F (2017) Conjunction of radial basis function interpolator and artificial intelligence models for time-space modeling of contaminant transport in porous media. J Hydrol 548:569–587

    Google Scholar 

  • Olatomiwa L, Mekhilef S, Shamshirband S, Mohammadi K, Petković D, Sudheer Ch (2015) A support vector machine–firefly algorithm-based model for global solar radiation prediction. Solar Energy 115:632–644

    Google Scholar 

  • Patel A, Joshi G (2017) Modeling of rainfall-runoff correlations using artificial neural network—a case study of Dharoi Watershed of a Sabarmati River Basin, India. Civ Eng J 3(2):78–87

    Google Scholar 

  • Qiwei L, Liang L, Li J, Wu SH, Liu J (2016) Modeling and analysis on cushion characteristics of fast and high-flow-rate hydraulic cylinder. Math Probl Eng 17:66

    Google Scholar 

  • Raid S, Mania J (2004) Rainfall-runoff model using an artificial neural network approach. Math Comput Model 40:839–846

    MATH  Google Scholar 

  • Rajaee T, Mirbagheri SA, Zounemat-Kermani M, Nourani V (2009) Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models. Sci Total Environ 407(17):4916–4927

    Google Scholar 

  • 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):613–627

    Google Scholar 

  • Saghafian B, Meghdadi AR, Sima S (2014) Application of the WEPP model to determine sources of run-off and sediment in a forested watershed. Hydrol Process 29(4):481–497. https://doi.org/10.1002/hyp.10168

    Article  Google Scholar 

  • Schetinin V (2003) A learning algorithm for evolving cascade neural networks. Neural Process Lett 17:21–31

    Google Scholar 

  • Schneider W (2018) On basic equations and kinematic-wave theory of separation processes in suspensions with gravity, centrifugal and Coriolis forces. Acta Mech 229:779–794. https://doi.org/10.1007/s00707-017-1998-x

    Article  MathSciNet  MATH  Google Scholar 

  • Singh VP, Tayfur G (2008) Kinematic wave theory for transient bed sediment waves in Alluvial Rivers. J Hydrol Eng 13(5):297–304. https://doi.org/10.1061/(asce)1084-0699(2008)13:5(297)

    Article  Google Scholar 

  • Singh HV, Panuska J, Thompson AM (2017) Estimating sediment delivery ratios for grassed waterways using WEPP. Land Degrad Dev 28(7):2051–2061. https://doi.org/10.1002/ldr.2727

    Article  Google Scholar 

  • Sudheer KP, Gosain AK, Ramasastri KS (2003) Estimating actual evapotranspiration from limited climatic data using neural computing technique. J Irrig Drain Eng 129(3):214–218

    Google Scholar 

  • Sudheer Ch, Sohani SK, Kumar D, Malik A, Chahar BR, Nema AK, Panigrahi BK, Dhiman RC (2014) A support vector machine-firefly algorithm based forecasting model to determine malaria transmission. Neurocomputing 129:279–288

    Google Scholar 

  • Trajkovic S (2005) Temperature-based approaches for estimating reference evapotranspiration. J Irrig Drain Eng 131(4):316–323

    Google Scholar 

  • Wang X, Feng Y, Ning Z, Hu X, Kong X, Hu B, Guo Y (2020) A collective filtering based content transmission scheme in edge of vehicles. Inf Sci 506:161–173. https://doi.org/10.1016/j.ins.2019.07.083

    Article  Google Scholar 

  • Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses—a guide to conservation planning. USDA Ag Handb 537:58p

    Google Scholar 

  • Yadav A, Chatterjee S, Equeenuddin SM (2018) Prediction of suspended sediment yield by artificial neural network and traditional mathematical model in Mahanadi river basin, India. Sustain Water Resour Manag 4:745–759. https://doi.org/10.1007/s40899-017-0160-1

    Article  Google Scholar 

  • Yang XS (2009) Firefly algorithms for multimodal optimization. stochastic algorithms: foundations and applications, SAGA 2009. Lect Notes Comput Sci 5792:169–178

    Google Scholar 

  • Yang XS (2010) Firefly algorithm, stochastic test functions and design optimization. Int J Bio-Inspired Comput 2(2):78–84

    Google Scholar 

  • Yu-hong D, Cai-xia K (2013) A nonlinear conjugate gradient algorithm with an optimal property and an improved Wolfe line search. Soc Ind Appl Math 23(1):296–320

    MathSciNet  MATH  Google Scholar 

  • Yuksel A, Gundogan R, Akay AE (2008) Using the remote sensing and GIS technology for erosion risk mapping of Kartalkaya Dam Watershed in Kahramanmaras, Turkey. Sensors 8(8):4851–4865. https://doi.org/10.3390/s8084851

    Article  Google Scholar 

Download references

Acknowledgements

The authors thankfully acknowledge the Deanship of Scientific Research, King Khalid University, Abha, Kingdom of Saudi Arabia, for funding the research grant number RGP. 1/174/42.

Author information

Authors and Affiliations

  1. Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Sarita Gajbhiye Meshram

  2. Department of Post Graduate Studies and Research in Mathematics, Jaywanti Haksar Government Post-Graduation College, College of Chhindwara University, Betul, Madhya Pradesh, 460001, India

    Chandrashekhar Meshram

  3. Water Resources Engineering, University of Tehran, Karaj, Iran

    Fateme Akhoni Pourhosseini

  4. Civil Engineering Department, College of Engineering, King Khalid University, PO Box 394, Abha, 61421, Kingdom of Saudi Arabia

    Mohd Abul Hasan & Saiful Islam

Authors
  1. Sarita Gajbhiye Meshram
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chandrashekhar Meshram
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fateme Akhoni Pourhosseini
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohd Abul Hasan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Saiful Islam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sarita Gajbhiye Meshram.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Meshram, S.G., Meshram, C., Pourhosseini, F.A. et al. A Multi-Layer Perceptron (MLP)-Fire Fly Algorithm (FFA)-based model for sediment prediction. Soft Comput 26, 911–920 (2022). https://doi.org/10.1007/s00500-021-06281-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-021-06281-4

Keywords

Search

Navigation