Predicting Seepage of Earth Dams Using Neural Network and Genetic Algorithm

Xin Ying Miao; Jin Kui Chu; Jing Qiao; Ling Han Zhang

doi:10.4028/www.scientific.net/AMR.403-408.3081

Paper Titles

Image Enhancement Based on Space Domain with MATLAB
p.3063

An Integration Framework of Product Design Knowledge Oriented to Collaborative Design
p.3067

A Practical Tool for Multiple Simulation Tasks of Gas Engine
p.3071

Wireless Transfer Mode Based on the Environmental Detection System
p.3077

Predicting Seepage of Earth Dams Using Neural Network and Genetic Algorithm
p.3081

Research and Implementation of Management System of Laboratory
p.3086

Performance Assessment on Two Kinds of GA-Based Neural Networks in Fault Diagnosis
p.3090

Dynamical Assembly for Naked-Eye-Stereoscopic LCD Screen
p.3095

The Research on 4WS Vehicle Steering Stability by Modeling and Simulation
p.3099

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 403-408Predicting Seepage of Earth Dams Using Neural...

Predicting Seepage of Earth Dams Using Neural Network and Genetic Algorithm

Abstract:

Measurements of seepage are fundamental for earth dam surveillance. However, it is difficult to establish an effective and practical dam seepage prediction model due to the nonlinearity between seepage and its influencing factors. Genetic Algorithm for Levenberg-Marquardt(GA-LM), a new neural network(NN) model has been developed for predicting the seepage of an earth dam in China using 381 databases of field data (of which 366 in 2008 were used for training and 15 in 2009 for testing). Genetic algorithm(GA) is an ecological system algorithm, which was adopted to optimize the NN structure. Levenberg-Marquardt (LM) algorithm was originally designed to serve as an intermediate optimization algorithm between the Gauss-Newton(GN) method and the gradient descent algorithm, which was used to train NN. The predicted seepage values using GA-LM model are in good agreement with the field data. It is demonstrated here that the model is capable of predicting the seepage of earth dams accurately. The performance of GA-LM has been compared with that of conventional Back-Propagation(BP) algorithm and LM algorithm with trial-and-error approach. The comparison indicates that the GA-LM model can offer stronger and better performance than conventional NNs when used as a quick interpolation and extrapolation tool.