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Advances in Meteorology
Volume 2015, Article ID 273730, 12 pages
Research Article

Artificial Neural Network Modeling for Spatial and Temporal Variations of Pore-Water Pressure Responses to Rainfall

1Civil Engineering Department, Universiti Teknologi Petronas, 31750 Tronoh, Perak Darul Ridzuan, Malaysia
2City of Moose Jaw, 228 Main Street North, Moose Jaw, SK, Canada S6H 3J8
3School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798
4Engineering Physics Department, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia

Received 17 March 2014; Revised 28 June 2014; Accepted 8 August 2014

Academic Editor: Dimitrios Katsanos

Copyright © 2015 M. R. Mustafa et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Knowledge of spatial and temporal variations of soil pore-water pressure in a slope is vital in hydrogeological and hillslope related processes (i.e., slope failure, slope stability analysis, etc.). Measurements of soil pore-water pressure data are challenging, expensive, time consuming, and difficult task. This paper evaluates the applicability of artificial neural network (ANN) technique for modeling soil pore-water pressure variations at multiple soil depths from the knowledge of rainfall patterns. A multilayer perceptron neural network model was constructed using Levenberg-Marquardt training algorithm for prediction of soil pore-water pressure variations. Time series records of rainfall and pore-water pressures at soil depth of 0.5 m were used to develop the ANN model. To investigate applicability of the model for prediction of spatial and temporal variations of pore-water pressure, the model was tested for the time series data of pore-water pressure at multiple soil depths (i.e., 0.5 m, 1.1 m, 1.7 m, 2.3 m, and 2.9 m). The performance of the ANN model was evaluated by root mean square error, mean absolute error, coefficient of correlation, and coefficient of efficiency. The results revealed that the ANN performed satisfactorily implying that the model can be used to examine the spatial and temporal behavior of time series of pore-water pressures with respect to multiple soil depths from knowledge of rainfall patterns and pore-water pressure with some antecedent conditions.