Artificial Neural Network Modeling for Spatial and Temporal Variations of Pore-Water Pressure Responses to Rainfall
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 difficu...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
|
| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2015/273730 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832563631024242688 |
|---|---|
| author | M. R. Mustafa R. B. Rezaur H. Rahardjo M. H. Isa A. Arif |
| author_facet | M. R. Mustafa R. B. Rezaur H. Rahardjo M. H. Isa A. Arif |
| author_sort | M. R. Mustafa |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-439b836ed6654f0fa4253204e0ec88dc |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-439b836ed6654f0fa4253204e0ec88dc2025-02-03T01:13:00ZengWileyAdvances in Meteorology1687-93091687-93172015-01-01201510.1155/2015/273730273730Artificial Neural Network Modeling for Spatial and Temporal Variations of Pore-Water Pressure Responses to RainfallM. R. Mustafa0R. B. Rezaur1H. Rahardjo2M. H. Isa3A. Arif4Civil Engineering Department, Universiti Teknologi Petronas, 31750 Tronoh, Perak Darul Ridzuan, MalaysiaCity of Moose Jaw, 228 Main Street North, Moose Jaw, SK, S6H 3J8, CanadaSchool of Civil and Environmental Engineering, Nanyang Technological University, 639798, SingaporeCivil Engineering Department, Universiti Teknologi Petronas, 31750 Tronoh, Perak Darul Ridzuan, MalaysiaEngineering Physics Department, Universitas Gadjah Mada, Yogyakarta 55281, IndonesiaKnowledge 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.http://dx.doi.org/10.1155/2015/273730 |
| spellingShingle | M. R. Mustafa R. B. Rezaur H. Rahardjo M. H. Isa A. Arif Artificial Neural Network Modeling for Spatial and Temporal Variations of Pore-Water Pressure Responses to Rainfall Advances in Meteorology |
| title | Artificial Neural Network Modeling for Spatial and Temporal Variations of Pore-Water Pressure Responses to Rainfall |
| title_full | Artificial Neural Network Modeling for Spatial and Temporal Variations of Pore-Water Pressure Responses to Rainfall |
| title_fullStr | Artificial Neural Network Modeling for Spatial and Temporal Variations of Pore-Water Pressure Responses to Rainfall |
| title_full_unstemmed | Artificial Neural Network Modeling for Spatial and Temporal Variations of Pore-Water Pressure Responses to Rainfall |
| title_short | Artificial Neural Network Modeling for Spatial and Temporal Variations of Pore-Water Pressure Responses to Rainfall |
| title_sort | artificial neural network modeling for spatial and temporal variations of pore water pressure responses to rainfall |
| url | http://dx.doi.org/10.1155/2015/273730 |
| work_keys_str_mv | AT mrmustafa artificialneuralnetworkmodelingforspatialandtemporalvariationsofporewaterpressureresponsestorainfall AT rbrezaur artificialneuralnetworkmodelingforspatialandtemporalvariationsofporewaterpressureresponsestorainfall AT hrahardjo artificialneuralnetworkmodelingforspatialandtemporalvariationsofporewaterpressureresponsestorainfall AT mhisa artificialneuralnetworkmodelingforspatialandtemporalvariationsofporewaterpressureresponsestorainfall AT aarif artificialneuralnetworkmodelingforspatialandtemporalvariationsofporewaterpressureresponsestorainfall |