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Predicting piezometric water level in dams via artificial neural networks

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Abstract

The safety control of dams is based on measurements of parameters of interest such as seepage flows, seepage water clarity, piezometric levels, water levels, pressures, deformations or movements, temperature variations, loading conditions, etc. Interpretation of these large sets of available data is very important for dam health monitoring and it is based on mathematical models. Modelling seepage through geological formations located near the dam site or dam bodies is a challenging task in dam engineering. The objective of this study is to develop a feedforward neural network (FNN) model to predict the piezometric water level in dams. An improved resilient propagation algorithm has been used to train the FNN. The measured data have been compared with the results of FNN models and multiple linear regression (MLR) models that have been widely used in analysis of the structural dam behaviour. The FNN and MLR models have been developed and tested using experimental data collected during 9 years. The results of this study show that FNN models can be a powerful and important tool which can be used to assess dams.

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Acknowledgments

The authors would like to thank the reviewers of this paper for their interesting comments and hints, which have helped to improve the quality of the paper. The part of this research is supported by the Ministry of Science in Serbia, Grants III41007 and TR37013.

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Correspondence to Vesna Ranković.

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Ranković, V., Novaković, A., Grujović, N. et al. Predicting piezometric water level in dams via artificial neural networks. Neural Comput & Applic 24, 1115–1121 (2014). https://doi.org/10.1007/s00521-012-1334-2

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  • DOI: https://doi.org/10.1007/s00521-012-1334-2

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