Predicting piezometric water level in dams via artificial neural networks

Autor:	Dejan Divac, Aleksandar Novaković, Vesna Ranković, Nikola Milivojević, Nenad Grujovic
Rok vydání:	2013
Předmět:	Water clarity Mathematical model Artificial neural network Artificial Intelligence Linear regression Computational Science and Engineering Feedforward neural network Geotechnical engineering Safety control Software Geology Water level
Zdroj:	Neural Computing and Applications. 24:1115-1121
ISSN:	1433-3058 0941-0643
DOI:	10.1007/s00521-012-1334-2
Popis:	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.
Databáze:	OpenAIRE
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