Predicting piezometric water level in dams via artificial neural networks View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2014-04

AUTHORS

Vesna Ranković, Aleksandar Novaković, Nenad Grujović, Dejan Divac, Nikola Milivojević

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. More... »

PAGES

1115-1121

References to SciGraph publications

Journal

TITLE

Neural Computing and Applications

ISSUE

5

VOLUME

24

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00521-012-1334-2

DOI

http://dx.doi.org/10.1007/s00521-012-1334-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1029646863


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