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Extreme Learning Machine Using Improved Gradient-Based Optimizer for Dam Seepage Prediction

  • Research Article-Computer Engineering and Computer Science
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Abstract

Seepage prediction is a vital part of the dam safety monitoring system. Traditional statistical models ignore the nonlinear characteristics of the measured variables, resulting in poor accuracy and stability. In this study, an optimized neural network model is constructed to predict the seepage extent of hydropower station dams. An improved gradient-based optimizer (IGBO) is proposed to increase the accuracy and reliability of extreme learning machine (ELM) model predictions. The IGBO introduces an initialization method with elite opposition-based learning to improve population diversity. A crossover operator and a nonlinear parameter are used in the IGBO to enhance the ability of local search and the probability of avoiding local optima. The performance of the IGBO-optimized ELM network (IGBO-ELM) was evaluated on 12 datasets. In addition, the comparison experimental results with actual monitoring data of concrete dams show that IGBO-ELM has strong generalization performance and accuracy among the other four optimization models.

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References

  1. Cao, E.; Bao, T.; Gu, C.; Li, H.; Liu, Y.; Hu, S.: A novel hybrid decomposition-ensemble prediction model for dam deformation. Appl. Sci. 10(16), 5700 (2020)

    Google Scholar 

  2. Parsaie, A.; Haghiabi, A.H.; Latif, S.D.; Tripathi, R.P.: Predictive modelling of piezometric head and seepage discharge in Earth dam using soft computational models. Environ. Sci. Pollut. Res. 28(43), 60842–60856 (2021)

    Google Scholar 

  3. Ishfaque, M.; Dai, Q.; Jadoon, K.; Shahzad, S.M.; Janjuhah, H.T.: Use of recurrent neural network with long short-term memory for seepage prediction at Tarbela dam, KP, Pakistan. Energies 15(9), 3123 (2022)

    Google Scholar 

  4. Xiong, Z.: Study on seepage analysis model controlled by rainfall. In International Conference on Computer Vision and Pattern Analysis (ICCPA 2021)(Vol. 12158, pp. 199–202). SPIE (2022)

  5. Nourani, V.; Behfar, N.; Dabrowska, D.; Zhang, Y.: The applications of soft computing methods for seepage modeling: a review. Water 13(23), 3384 (2021)

    Google Scholar 

  6. Seyed-Kolbadi, S.M.; Hariri-Ardebili, M.A.; Mirtaheri, M.; Pourkamali-Anaraki, F.: Instrumented health monitoring of an earth dam. Infrastructures 5(3), 26 (2020)

    Google Scholar 

  7. Lin, C.; Li, T.; Chen, S.; Liu, X.; Lin, C.; Liang, S.: Gaussian process regression-based forecasting model of dam deformation. Neural Comput. Appl. 31(12), 8503–8518 (2019)

    Google Scholar 

  8. Bian, K.; Wu, Z.: Data-based model with EMD and a new model selection criterion for dam health monitoring. Eng. Struct. 260, 114171 (2022)

    Google Scholar 

  9. Yu, Y.; Liu, X.; Wang, E.; Fang, K.; Huang, L.: Dam safety evaluation based on multiple linear regression and numerical simulation. Rock Mech. Rock Eng. 51(8), 2451–2467 (2018)

    Google Scholar 

  10. Tatin, M.; Briffaut, M.; Dufour, F.; Simon, A.; Fabre, J.P.: Statistical modelling of thermal displacements for concrete dams: influence of water temperature profile and dam thickness profile. Eng. Struct. 165, 63–75 (2018)

    Google Scholar 

  11. Salazar, F.; Toledo, M.A.; Oñate, E.; Morán, R.: An empirical comparison of machine learning techniques for dam behaviour modelling. Struct. Saf. 56, 9–17 (2015)

    Google Scholar 

  12. Li, B.; Yang, J.; Hu, D.: Dam monitoring data analysis methods: a literature review. Struct. Control. Health Monit. 27(3), e2501 (2020)

    Google Scholar 

  13. Kumar, P.M.; Saravanakumar, R.; Karthick, A.; Mohanavel, V.: Artificial neural network-based output power prediction of grid-connected semitransparent photovoltaic system. Environ. Sci. Pollut. Res. 29， 10173–10182 (2022)

  14. El Bilali, A.; Moukhliss, M.; Taleb, A.; Nafii, A.; Alabjah, B.; Brouziyne, Y.; Mhamed, M.: Predicting daily pore water pressure in embankment dam: empowering machine learning-based modeling. Environ. Sci. Pollut. Res. 29， 47382–47398 (2022)

  15. Ziggah, Y.Y.; Issaka, Y.; Laari, P.B.: Evaluation of different artificial intelligent methods for predicting dam piezometric water level. Model. Earth Syst. Environ. 8(2), 2715–2731 (2022)

    Google Scholar 

  16. Ibrahim, K.S.M.H.; Huang, Y.F.; Ahmed, A.N.; Koo, C.H.; El-Shafie, A.: A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting. Alex. Eng. J. 61(1), 279–303 (2022)

    Google Scholar 

  17. Li, Y.; Bao, T.; Gao, Z.; Shu, X.; Zhang, K.; Xie, L.; Zhang, Z.: A new dam structural response estimation paradigm powered by deep learning and transfer learning techniques. Struct. Health Monit. 21(3), 770–787 (2022)

    Google Scholar 

  18. Nguyen-Le, V.; Shin, H.: Artificial neural network prediction models for Montney shale gas production profile based on reservoir and fracture network parameters. Energy 244, 123150 (2022)

    Google Scholar 

  19. Tam, V.W.; Butera, A.; Le, K.N.; da Silva, L.C.; Evangelista, A.C.: A prediction model for compressive strength of CO2 concrete using regression analysis and artificial neural networks. Constr. Build. Mater. 324, 126689 (2022)

    Google Scholar 

  20. Bui, D.T.; Tsangaratos, P.; Nguyen, V.T.; Van Liem, N.; Trinh, P.T.: Comparing the prediction performance of a deep learning neural network model with conventional machine learning models in landslide susceptibility assessment. CATENA 188, 104426 (2020)

    Google Scholar 

  21. Huang, G.B.; Wang, D.H.; Lan, Y.: Extreme learning machines: A survey. Int. J. Mach. Learn. Cybern. 2(2), 107–122 (2011)

    Google Scholar 

  22. Huang, G.B.; Zhu, Q.Y.; Siew, C.K.: Extreme learning machine: Theory and applications. Neurocomputing 70(1–3), 489–501 (2006)

    Google Scholar 

  23. Yaseen, Z.M.; Sulaiman, S.O.; Deo, R.C.; Chau, K.W.: An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction. J. Hydrol. 569, 387–408 (2019)

    Google Scholar 

  24. Ali, M.; Prasad, R.: Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition. Renew. Sustain. Energy Rev. 104, 281–295 (2019)

    Google Scholar 

  25. Han, Y.; Liu, S.; Cong, D.; Geng, Z.; Fan, J.; Gao, J.; Pan, T.: Resource optimization model using novel extreme learning machine with t-distributed stochastic neighbor embedding: Application to complex industrial processes. Energy 225, 120255 (2021)

    Google Scholar 

  26. Faris, H.; Mirjalili, S.; Aljarah, I.; Mafarja, M.; Heidari, A.A.: Salp swarm algorithm: theory, literature review, and application in extreme learning machines. In: Mirjalili, S., Song Dong, J., Lewis, A. (eds.) Nature-Inspired Optimizers. Studies in Computational Intelligence, vol. 811, pp. 185–199. Springer, Cham (2020)

  27. Gao, Y.; Zhou, Y.; Luo, Q.: An efficient binary equilibrium optimizer algorithm for feature selection. IEEE Access 8, 140936–140963 (2020)

    Google Scholar 

  28. Tang, C.; Zhou, Y.; Tang, Z.; Luo, Q.: Teaching-learning-based pathfinder algorithm for function and engineering optimization problems. Appl. Intell. 51(7), 5040–5066 (2021)

    Google Scholar 

  29. Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver press, UK (2010)

    Google Scholar 

  30. Mirjalili, S.: Genetic algorithm. In: Evolutionary Algorithms and Neural Networks. Studies in Computational Intelligence, vol. 780, pp. 43–55. Springer, Cham (2019)

  31. Price, K.V.: Differential evolution. In: Zelinka, I., Snášel, V., Abraham, A. (eds.) Handbook of Optimization. Intelligent Systems Reference Library, vol. 38, pp. 187–214. Springer, Berlin, Heidelberg (2013)

  32. Poli, R.; Kennedy, J.; Blackwell, T.: Particle swarm optimization. Swarm Intell. 1(1), 33–57 (2007)

    Google Scholar 

  33. Mirjalili, S.; Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)

    Google Scholar 

  34. Mirjalili, S.; Mirjalili, S.M.; Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Google Scholar 

  35. Rashedi, E.; Nezamabadi-Pour, H.; Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009)

    MATH  Google Scholar 

  36. Mirjalili, S.: SCA: a sine cosine algorithm for solving optimization problems. Knowl. Based Syst. 96, 120–133 (2016)

    Google Scholar 

  37. Ahmadianfar, I.; Bozorg-Haddad, O.; Chu, X.: Gradient-based optimizer: a new metaheuristic optimization algorithm. Inf. Sci. 540, 131–159 (2020)

    MathSciNet  MATH  Google Scholar 

  38. Karaboga, D.; Basturk, B.: Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. In: Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds.) Foundations of Fuzzy Logic and Soft Computing. IFSA 2007. Lecture Notes in Computer Science, vol. 4529, pp, 789–798. Springer, Berlin (2007)

  39. Fan, C.; Zhou, Y.; Tang, Z.: Neighborhood centroid opposite-based learning Harris Hawks optimization for training neural networks. Evol. Intel. 14(4), 1847–1867 (2021)

    Google Scholar 

  40. Gan, Y.; Meng, B.; Chen, Y.; Sun, F.: An intelligent measurement method of the resonant frequency of ultrasonic scalpel transducers based on PSO-BP neural network. Measurement 190, 110680 (2022)

    Google Scholar 

  41. Aljarah, I.; Faris, H.; Mirjalili, S.: Optimizing connection weights in neural networks using the whale optimization algorithm. Soft. Comput. 22(1), 1–15 (2018)

    Google Scholar 

  42. Li, L.L.; Sun, J.; Tseng, M.L.; Li, Z.G.: Extreme learning machine optimized by whale optimization algorithm using insulated gate bipolar transistor module aging degree evaluation. Expert Syst. Appl. 127, 58–67 (2019)

    Google Scholar 

  43. Shi-fan, Q.; Jun-kun, T.; Yong-gang, Z.; Li-jun, W.; Ming-fei, Z.; Jun, T.; Qing, H.: Settlement prediction of foundation pit excavation based on the GWO-ELM model considering different states of influence. Adv. Civil Eng. 2021, 1 (2021)

    Google Scholar 

  44. Miao, X.Y.; Chu, J.K.; Qiao, J.; Zhang, L.H.: Predicting seepage of earth dams using neural network and genetic algorithm. In Advanced Materials Research Vol. 403, pp. 3081–3085. Trans Tech Publications Ltd. (2012)

  45. Zhang, K.; Gu, C.; Zhu, Y.; Chen, S.; Dai, B.; Li, Y.; Shu, X.: A novel seepage behavior prediction and lag process identification method for concrete dams using HGWO-XGBoost model. IEEE Access 9, 23311–23325 (2021)

    Google Scholar 

  46. Chi, S.; Ni, S.; Liu, Z.: Back analysis of the permeability coefficient of a high core rockfill dam based on a RBF neural network optimized using the PSO algorithm Mathematical Problems in Engineering (2015)

  47. Sherman, A.H.: On Newton-iterative methods for the solution of systems of nonlinear equations. SIAM J. Numer. Anal. 15(4), 755–771 (1978)

    MathSciNet  MATH  Google Scholar 

  48. Akyol, K.: Comparing of deep neural networks and extreme learning machines based on growing and pruning approach. Expert Syst. Appl. 140, 112875 (2020)

    Google Scholar 

  49. Tizhoosh, H.R.: Opposition-based learning: a new scheme for machine intelligence. In International conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC'06) (Vol. 1, pp. 695–701). IEEE (2005)

  50. Zhang, S.; Luo, Q.; Zhou, Y.: Hybrid grey wolf optimizer using elite opposition-based learning strategy and simplex method. Int. J. Comput. Intell. Appl. 16(02), 1750012 (2017)

    Google Scholar 

  51. Kaya, Y.; Uyar, M.: A novel crossover operator for genetic algorithms: ring crossover (2011) arXiv preprint arXiv:1105.0355.

  52. Niu, Y.; Yan, X.; Wang, Y.; Niu, Y. Dynamic opposite learning enhanced artificial ecosystem optimizer for IIR system identification. J. Supercomput. 78, 13040–13085 (2022)

  53. Han, F.; Yao, H.F.; Ling, Q.H.: An improved evolutionary extreme learning machine based on particle swarm optimization. Neurocomputing 116, 87–93 (2013)

    Google Scholar 

  54. Zhang, L.; Qin, Q.; Shang, Y.; Chen, S.; Zhao, S. Application of DE-ELM in analog circuit fault diagnosis. In 2016 Prognostics and System Health Management Conference (PHM-Chengdu) (pp. 1–6). IEEE (2016)

  55. Chao, W.; Khishe, M.; Mohammadi, M.; Karim, S.H.T.; Rashid, T.A.: Evolving deep convolutional neural network by hybrid sine-cosine and extreme learning machine for real-time COVID19 diagnosis from X-ray images (2021) arXiv preprint arXiv:2105.14192

  56. Sang, L.; Wang, J.C.; Sui, J.; Dziedzic, M.: A new approach for dam safety assessment using the extended cloud model. Water Resour. Manag. (2022). https://doi.org/10.1007/s11269-022-03124-1

  57. Chen, S.; Gu, C.; Lin, C.; Wang, Y.; Hariri-Ardebili, M.A.: Prediction, monitoring, and interpretation of dam leakage flow via adaptative kernel extreme learning machine. Measurement 166, 108161 (2020)

    Google Scholar 

  58. Chen, Y.; Zhang, X.; Karimian, H.; Xiao, G.; Huang, J.: A novel framework for prediction of dam deformation based on extreme learning machine and Lévy flight bat algorithm. J. Hydroinf. 23(5), 935–949 (2021)

    Google Scholar 

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Acknowledgements

This research is funded by the National Natural Science Foundation of China, Grant number U21A20464, 62066005, and Project of the Guangxi Science and Technology under Grant No. AD21196006.

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Authors and Affiliations

  1. College of Artificial Intelligence, Guangxi University for Nationalities, Nanning, 530006, China

    Li Lei, Yongquan Zhou, Huajuan Huang & Qifang Luo

  2. Xiangsihu College of Gunagxi University for Nationalities, Nanning, 532100, Guangxi, China

    Yongquan Zhou

  3. Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning, 530006, China

    Yongquan Zhou, Huajuan Huang & Qifang Luo

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  1. Li Lei
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Contributions

LL contributed to conceptualization, methodology, and writing—original draft. YZ contributed to supervision, writing—review and editing. HH contributed to validation, resources, writing—review and editing. QL contributed to validation, writing—review and editing, supervision.

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Correspondence to Yongquan Zhou.

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Lei, L., Zhou, Y., Huang, H. et al. Extreme Learning Machine Using Improved Gradient-Based Optimizer for Dam Seepage Prediction. Arab J Sci Eng 48, 9693–9712 (2023). https://doi.org/10.1007/s13369-022-07300-8

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