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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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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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