Predicting piezometric water level in dams via artificial neural networks

Vesna Rankovic, Aleksandar Novakovic, Nenad Grujovic, Dejan Divac, Nikola Milivojevic

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)1115-1121
JournalNeural Computing and Applications
Volume24
Issue number5
Early online date12 Jan 2013
DOIs
Publication statusPublished - Apr 2014

Keywords

  • Dam
  • Piezometric water level
  • Neural Network
  • IRPROP+

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