The effect of antecedent conditions on the prediction of soil pore-water pressure (PWP) using Artificial Neural Network (ANN) was evaluated using a multilayer feed forward (MLFF) type ANN model. The Scaled Conjugate Gradient (SCG) training algorithm was used for training the ANN. Time series data of rainfall and PWP was used for training and testing the ANN model. In the training stage, time series of rainfall was used as input data and corresponding time series of PWP was used as the target output. In the testing stage, data from a different time period was used as input and the corresponding time series of pore-water pressure was predicted. The performance of the model was evaluated using statistical measures of root mean square error and coefficient of determination. The results of the model prediction revealed that when antecedent conditions (past rainfall and past pore-water pressures) are included in the model input data, the prediction accuracy improves significantly.
© 2012 The Author(s). This paper was published in Modern Applied Science and is made available as an electronic reprint (preprint) with permission of the Author(s). The paper can be found at the following official DOI: http://dx.doi.org/10.5539/mas.v6n2p6. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.