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Hybrid models for suspended sediment prediction: optimized random forest and multi-layer perceptron through genetic algorithm and stochastic gradient descent methods

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Abstract

Owing to the nonlinear and non-stationary nature of the suspended sediment transport in rivers, suspended sediment concentration (SSC) modeling is a challenging task in environmental engineering. Investigation of SSC is of paramount importance in river morphology and hydraulic structures operation. To this end, for SSC modeling, first random forest (RF) and multi-layer perceptron (MLP) standalone models were developed, and then, they were optimized with genetic algorithm (GA) and stochastic gradient descent (SGD) to develop GA-MLP, GA-RF, SGD-MLP, and SGD-RF hybrid models. Variety of input scenarios are implemented for SSC prediction to find the best input combination. The streamflow and SSC data collected from two stations of Minnesota and San Joaquin rivers, respectively, located at South Dakota and California are utilized in the current study. Accuracies of the developed models are examined by means of three performance criteria of correlation coefficient (CC), scattered index (SI), and Willmott’s index of agreement (WI). A significant promotion in accuracy of hybrid models has been seen in contrast to their standalone counterparts. As can be deduced from the results, GA-MLP-5 and GA-RF-5 models with CC of 0.950 and 0.944, SI of 0.290 and 0.308, and WI of 0.974 and 0.971, respectively, were found as best models for prediction of SSC at Minnesota river. The developed SGD-MLP-5 and SGD-RF-5 models with CC of 0.900 and 0.901, SI of 0.339 and 0.339, and WI of 0.945 and 0.946, respectively, gave accurate results at San Joaquin river. Through the application of SGD algorithm, the adaptive learning rate, epochs, rho, L1 and L2 were activated and presumed as 0.004, 10, 1, 0.000009 and 0, respectively. The ExpRectifier was considered as san activation operation due to its better efficiency in comparison with its alternatives for predicting SSC in SGD-MLP model. According to the results, the fifth scenario that incorporates SSCt–1, SSCt–2, Qt, Qt–1, and Qt–2 were found superior for SSC modeling in the studied rivers. The recommended hybrid algorithms based on GA and SGD optimization algorithms are proposed as practical tools for solving complex environmental problems.

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Data availability

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

References

  1. Adnan MN, Islam MZ (2016) Optimizing the number of trees in a decision forest to discover a subforest with high ensemble accuracy using a genetic algorithm. Knowl-Based Syst 110:86–97

    Article  Google Scholar 

  2. Altunkaynak A (2009) Sediment load prediction by genetic algorithms. Adv Eng Softw 40(9):928–934

    Article  MATH  Google Scholar 

  3. Asadi E, Isazadeh M, Samadianfard S, Ramli MF, Mosavi A, Nabipour N, Shamshirband S, Hajnal E, Chau KW (2020) Groundwater quality assessment for sustainable drinking and irrigation. Sustainability 12:177

    Article  Google Scholar 

  4. Bäck T, Fogel DB, Michalewicz Z (2000) Evolutionary computation 1: Basic algorithms and operators. Institute of Physics Pub, Bristol

  5. Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  MATH  Google Scholar 

  6. Chong EK, Zak SH (2013) An introduction to optimization. Wiley, NY

    MATH  Google Scholar 

  7. Choubin B (2020) Spatial hazard assessment of the PM10 using machine learning models in Barcelona Spain. Sci Total Environ 701:134474

    Article  Google Scholar 

  8. 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 367:52–61

    Article  Google Scholar 

  9. Cutler A, Cutler DR, Stevens JR (2011) Random forests. In: Ensemble Machine Learning, pp 157–176

  10. Dang MN (2021) Integration of ANFIS with PCA and DWT for daily suspended sediment concentration prediction. Water SA 47:200–209

    Google Scholar 

  11. Dodangeh E, Choubin B, Eigdir AN (2019) Integrated machine learning methods with resampling algorithms for flood susceptibility prediction. Sci Total Environ 9:135983

    Google Scholar 

  12. Douglas RK, Nawar S, Alamar MC, Mouazen AM, Coulon F (2018) Rapid prediction of total petroleum hydrocarbons concentration in contaminated soil using vis-NIR spectroscopy and regression techniques. Sci Total Environ 616–617:147–155

    Article  Google Scholar 

  13. Du KL, Swamy MN (2006) Neural networks in a soft computing framework. Springer Science & Business Media, Berlin

    Google Scholar 

  14. Frings RM, Kleinhans MG (2008) Complex variations in sediment transport at three large river bifurcations during discharge waves in the river Rhine. Sedimentology 55:1145–1171

    Article  Google Scholar 

  15. Gallagher K, Sambridge M (1994) Genetic algorithms: a powerful tool for largescale nonlinear optimization problems. Comput Geosci 20(7):1229–1236

    Article  Google Scholar 

  16. Ghose D, Samantaray S (2018) Modelling sediment concentration using back propagation neural network and regression coupled with genetic algorithm. Procedia Comput Sci 125:85–92

    Article  Google Scholar 

  17. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley Longman Publishing Co., Inc.

  18. Holland JH (1992) Genetic algorithms. Sci Am 267:66–72

    Article  Google Scholar 

  19. Kargar K, Samadianfard S, Parsa J, Nabipour N, Shamshirband S, Mosavi A, Chau KW (2020) Estimating longitudinal dispersion coefficient in natural streams using empirical models and machine learning algorithms. Eng Appl Comput Fluid 14(1):311–322

    Google Scholar 

  20. Kisi O (2010) River suspended sediment concentration modeling using a neural differential evolution approach. J Hydrol 389:227–235

    Article  Google Scholar 

  21. Kisi O, Guven A (2010) A machine code-based genetic programming for suspended sediment concentration estimation. Adv Eng Softw 41:939–945

    Article  MATH  Google Scholar 

  22. Kisi O, Zounemat-Kermani M (2016) Suspended sediment modeling using neurofuzzy embedded fuzzy c-means clustering technique. Water Resour Manage 30:3979–3994

    Article  Google Scholar 

  23. Kumar D, Pandey A, Sharma N, Flügel WA (2016) Daily suspended sediment simulation using machine learning approach. CATENA 138:77–90

    Article  Google Scholar 

  24. 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 186:181–190

    Article  Google Scholar 

  25. Liu QJ, Zhang HY, Gao KT, Xu B, Wu JZ, Fang NF (2019) Time-frequency analysis and simulation of the watershed suspended sediment concentration based on the Hilbert-Huang transform (HHT) and artificial neural network (ANN) methods: A case study in the Loess Plateau of China. CATENA 179:107–118

    Article  Google Scholar 

  26. Malik A, Kumar A, Piri J (2017) Daily suspended sediment concentration simulation using hydrological data of Pranhita River Basin, India. Comput Electron Agric 138:20–28

    Article  Google Scholar 

  27. McBean EA, Al-Nassri S (1988) Uncertainty in suspended sediment transport curves. J Hydrol Eng, ASCE 114(1):63–74

    Article  Google Scholar 

  28. Mehri Y, Nasrabadi M, Omid MH (2021) Prediction of suspended sediment distributions using data mining algorithms. Ain Shams Engineering Journal

  29. Meshram SG, Safari MJS, Khosravi K, Meshram C (2021) Iterative classifier optimizer-based pace regression and random forest hybrid models for suspended sediment load prediction. Environ Sci Pollut Res 28(9):11637–11649

    Article  Google Scholar 

  30. 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

    Article  Google Scholar 

  31. Prasad AM, Iverson LR, Andy L (2006) Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9(2):181–199

    Article  Google Scholar 

  32. Qasem SN, Samadianfard S, Sadri Nahand H, Mosavi A, Shamshirband S, Chau KW (2019) Estimating daily dew point temperature using machine learning algorithms. Water 11:582

    Article  Google Scholar 

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

    Article  Google Scholar 

  34. Robbins H, Monro S (1951) A Stochastic Approximation Method. Ann Math Stat 22:400–407

    Article  MathSciNet  MATH  Google Scholar 

  35. Rodriguez-Galiano V, Ghimire B, Rogan J, Chica-Olmo M, Rigol-Sanchez J (2012) An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J Photogramm Remote Sens 67:93–104

    Article  Google Scholar 

  36. Roushangar K, Aghajani N, Ghasempour R, Alizadeh F (2021) The potential of ensemble WT-EEMD-kernel extreme learning machine techniques for prediction suspended sediment concentration in successive points of a river. J Hydroinf 23:655–670

    Article  Google Scholar 

  37. Safari MJS (2020) Hybridization of multivariate adaptive regression splines and random forest models with an empirical equation for sediment deposition prediction in open channel flow. J Hydrol 590:125392

    Article  Google Scholar 

  38. Safari MJS, Aksoy H, Mohammadi M (2016) Artificial neural network and regression models for flow velocity at sediment incipient deposition. J Hydrol 541:1420–1429

    Article  Google Scholar 

  39. Samadianfard S, Hashemi S, Kargar K, Izadyar M, Mostafaeipour A, Mosavi A, Nabipour N, Shamshirband S (2020) Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm. Energy Rep 6:1147–1159

    Article  Google Scholar 

  40. Samantaray S, Sahoo A (2021) Prediction of suspended sediment concentration using hybrid SVM-WOA approaches. Geocarto International.

  41. Shabani S, Samadianfard S, Sattari MT, Mosavi A, Shamshirband S, Kmet T, Várkonyi-Kóczy AR (2020) Modeling pan evaporation using gaussian process regression K-nearest neighbors random forest and support vector machines. Comparative Anal Atmos 11:66

    Google Scholar 

  42. Shirzad A, Safari MJS (2019) Pipe failure rate prediction in water distribution networks using multivariate adaptive regression splines and random forest techniques. Urban Water J 16(9):653–661

    Article  Google Scholar 

  43. Singh N, Chakrapani GJ (2015) ANN modelling of sediment concentration in the dynamic glacial environment of Gangotri in Himalaya. Environ Monit Assess 187(8):494

    Article  Google Scholar 

  44. Sivakumar B, Jayawardena AW (2002) An investigation of the presence of low-dimensional chaotic behaviour in the sediment transport phenomenon. Hydrol Sci J 47:37–41

    Article  Google Scholar 

  45. Taddy M (2019) Business data science: Combining machine learning and economics to optimize, automate, and accelerate business decisions. McGraw-Hill, New York

    Google Scholar 

  46. Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res: Atmos 106:7183–7192

    Article  Google Scholar 

  47. Verstraeten G, Poesen J (2001) Factors controlling sediment yield from small intensively cultivated catchments in a temperate humid climate. Geomorphology 40:123–144

    Article  Google Scholar 

  48. Ward P, Balen RT, Verstraeten G, Renssen H, Vandenberghe J (2009) The impact of land use and climate change on late Holocene and future suspended sediment yield of the Meuse catchment. Geomorphology 103:389–400

    Article  Google Scholar 

  49. Willmott CJ (1982) Some comments on the evaluation of model performance. Bull Am Meteor Soc 63:1309–1313

    Article  Google Scholar 

  50. Zhang FX, Wai OWH, Jiang YW (2010) Prediction of sediment transportation indeep bay (Hong Kong) using genetic algorithm. J Hydrodyn, Ser B 22(5):599–604

    Google Scholar 

  51. Zhou ZH, Wu J, Tang W (2002) Ensembling neural networks: many could be better than all. Artif Intell 137:239–263

    Article  MathSciNet  MATH  Google Scholar 

  52. Zounemat-Kermani M, Kisi O, Adamowski J, Ramezani-Charmahineh A (2016) Evaluation of data driven models for river suspended sediment concentration modeling. J Hydrol 535:457–472

    Article  Google Scholar 

  53. Zounemat-Kermani M, Seo Y, Kim S, Ghorbani MA, Samadianfard S, Naghshara S, Kim NW, Singh VP (2019) Can decomposition approaches always enhance soft computing models? Predicting the dissolved oxygen concentration in the St. Johns River, Florida. Applied Sciences, 9:2534.

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Authors and Affiliations

  1. Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

    Saeed Samadianfard, Sadra Shadkani, Sajjad Hashemi & Akram Abbaspour

  2. Department of Civil Engineering, Ryerson University, Toronto, ON, Canada

    Katayoun Kargar

  3. Department of Civil Engineering, Yaşar University, Izmir, Turkey

    Mir Jafar Sadegh Safari

Authors
  1. Saeed Samadianfard
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  2. Katayoun Kargar
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  3. Sadra Shadkani
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  4. Sajjad Hashemi
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  5. Akram Abbaspour
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  6. Mir Jafar Sadegh Safari
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Contributions

The author contributions are listed as follows: (1) Conceptualization: Saeed Samadianfard, Mir Jafar Sadegh Safari, (2) Data curation: Sadra Shadkani, Sajjad Hashemi, (3) Formal analysis: Saeed Samadianfard, Katayoun Kargar, Sadra Shadkani, (4) Investigation: Saeed Samadianfard, Katayoun Kargar, Akram Abbaspour, (5) Methodology: Katayoun Kargar, Sadra Shadkani, Sajjad Hashemi, (6) Resources: Saeed Samadianfard, Akram Abbaspour, Mir Jafar Sadegh Safari, (7) Software: Sadra Shadkani, Sajjad Hashemi, (8) Supervision: Saeed Samadianfard, Akram Abbaspour, Mir Jafar Sadegh Safari, (9) Validation: Saeed Samadianfard, Mir Jafar Sadegh Safari, (10) Visualization: Saeed Samadianfard, Sadra Shadkani, Sajjad Hashemi, (11) Writing—original draft: Saeed Samadianfard, Katayoun Kargar, (12) Writing—review & editing: Saeed Samadianfard, Mir Jafar Sadegh Safari

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Correspondence to Saeed Samadianfard.

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Samadianfard, S., Kargar, K., Shadkani, S. et al. Hybrid models for suspended sediment prediction: optimized random forest and multi-layer perceptron through genetic algorithm and stochastic gradient descent methods. Neural Comput & Applic 34, 3033–3051 (2022). https://doi.org/10.1007/s00521-021-06550-1

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