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A novel hybrid artificial intelligent approach based on neural fuzzy inference model and particle swarm optimization for horizontal displacement modeling of hydropower dam

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Abstract

Horizontal displacement of hydropower dams is a typical nonlinear time-varying behavior that is difficult to forecast with high accuracy. This paper proposes a novel hybrid artificial intelligent approach, namely swarm optimized neural fuzzy inference system (SONFIS), for modeling and forecasting of the horizontal displacement of hydropower dams. In the proposed model, neural fuzzy inference system is used to create a regression model whereas Particle swarm optimization is employed to search the best parameters for the model. In this work, time series monitoring data (horizontal displacement, air temperature, upstream reservoir water level, and dam aging) measured for 11 years (1999–2010) of the Hoa Binh hydropower dam were selected as a case study. The data were then split into a ratio of 70:30 for developing and validating the hybrid model. The performance of the resulting model was assessed using RMSE, MAE, and R 2. Experimental results show that the proposed SONFIS model performed well on both the training and validation datasets. The results were then compared with those derived from current state-of-the-art benchmark methods using the same data, such as support vector regression, multilayer perceptron neural networks, Gaussian processes, and Random forests. In addition, results from a Different evolution-based neural fuzzy model are included. Since the performance of the SONFIS model outperforms these benchmark models with the monitoring data at hand, the proposed model, therefore, is a promising tool for modeling horizontal displacement of hydropower dams.

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Acknowledgements

This research was funded by the China Scholarship Council (CSC) and partially supported by the Project 322 (Vietnam). The data analysis and write-up were carried out as a part of the first author’s Ph.D. studies at the School of Geodesy and Geomatics, Wuhan University, P. R. of China. We would like to thank two anonymous reviewers for their constructive and valuable comments on the earlier version of the manuscript.

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Authors and Affiliations

  1. School of Geodesy and Geomatics, Wuhan University, No. 129 Luo Yu Road, Wuhan, 430072, Hubei, China

    Kien-Trinh Thi Bui & Jingui Zou

  2. Geomatics Center, Water Resources University, No. 175 Tay Son Street, Hanoi, Vietnam

    Kien-Trinh Thi Bui

  3. Geographic Information System Group, University College of Southeast Norway, Hallvard Eikas Plass1, 3800, Bø i Telemark, Norway

    Dieu Tien Bui

  4. Faculty of Surveying and Mapping, Le Quy Don Technical University, 100 Hoang Quoc Viet Street, Hanoi, Vietnam

    Chinh Van Doan

  5. Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, P.O. Box 5003 IMT, 1432, Aas, Norway

    Inge Revhaug

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  1. Kien-Trinh Thi Bui
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  2. Dieu Tien Bui
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Correspondence to Dieu Tien Bui.

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Bui, KT.T., Tien Bui, D., Zou, J. et al. A novel hybrid artificial intelligent approach based on neural fuzzy inference model and particle swarm optimization for horizontal displacement modeling of hydropower dam. Neural Comput & Applic 29, 1495–1506 (2018). https://doi.org/10.1007/s00521-016-2666-0

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