This is to certify that I am responsible for the work presented in this project, that the original work is my own, except as specified in the references and acknowledgments, and that the original work contained herein was not undertaken or done by unspecified sources or persons . Several problems have been identified in obtaining pore water pressure readings, which require time and labor intensive field instrumentation. However, the development of soft computing nowadays has become the focus as an alternative technology to monitor pore water pressure changes in soil, and the purpose in this study is to use support vector machine.
Then, a study has been conducted on developing a model to predict the soil pore water using polynomial kernel support vector machine and to evaluate the performance of the model. A significant good correlation between observed and predicted pore water pressure has been found at the end of the studies with performance evaluation of R2 and RMSE of 0.93 and 0.60 A slope respectively in Universiti Teknologi PETRONAS, Perak has been selected as the scope of study to predict the soil pore water pressure . I would also like to express my gratitude to Universiti Teknologi PETRONAS for providing opportunities to gain exposure in connection with the research.
Their encouragement and motivation have been one of the keys that continue to strengthen my spirit to get through this period of project completion. Words fail to express my deepest gratitude to my project supervisor, Dr. Muhammad Raza Ul Mustafa, for the endless support in ensuring that this project runs smoothly, both for the technical and non-technical sector. Finally, our sincere thanks go to all parties directly or indirectly involved in this research project.
RMSE – Root Mean Square Error R2 – Coefficient of Determination RBF – Radial Basis Function SM – Sample Mean.
INTRODUCTION
BACKGROUND
This field was chosen because the existing field is already equipped with a tensiometer and rain gauge that will be used to measure pore water pressure (PWP) and rain intensity respectively.
PROBLEM STATEMENT
- Objective
- Scope of Study
To predict soil pore water pressure responses to rainfall events using the polynomial kernel support vector machine (SVM) model. The development of the SVM model will use antecedent data of pore water pressure and rainfall. The observed data is obtained from data collected from the instrumented slope date January 1, 2015 to March 31, 2015 (3 months).
To evaluate the performance of the polynomial kernel support vector machine model in predicting the pore water pressure responses to rainfall events. The reason why the slope was chosen is because of the feasibility of using the tensiometer and rain gauge setup that deliberately collects the data of soil pore water pressure and rainfall. Currently, there are few known or basic kernels available other than the polynomial kernel.
The purpose of using a polynomial kernel is because it can perform the process much faster compared to other available kernels.
LITERATURE REVIEW AND THEORY
- SOIL PORE WATER PRESSURE
- SUPPORT VETOR MACHINE
- APPLICATION OF SOFT COMPUTING IN PREDICTING PWP
- RESEARCH METHODOLOGY
- PROJECT ACTIVITIES .1 Final Year Project 1
This was then followed with the development of SVM which is claimed to be the most successful method of classification in machine learning, and has been demonstrated in several studies to be much more robust in many classification and recognition fields than the next best method. SVR model is effective in providing an accurate and fast way to obtain pore water pressure response, which can be essential in systems where response information is urgently needed. Common methodology is listed to ensure the smoothness and efficiency of the entire study process.
In order to implement PWP prediction using SVM, a suitable slope was selected and equipped with the necessary rainfall collection instruments and. The slope is equipped with tensiometer which is equipped with transducer and data logger to collect data on soil pore water pressure and rain gauge to get rainfall data. Model development will cover data partitioning, defining input data and model structure, data normalization, model generation, and defining model performance evaluation criteria.
Thus, the data was divided into two categories, which are intended for training and testing. The reason the percentage of training data is higher than the testing data is because the model needs to be trained more to perform better in model testing. Meanwhile, the success of training algorithms depends on the complexity of the problem and the type to be modeled, the data used to train the network, and the architecture of the network (Rafik et. al, 2008).
Babangida N.M et al., (2016) has considered five PWP antecedent rainfalls, current and five antecedent rainfalls for the analysis and determination of the required feature which constitute up to eleven input features although the importance of the input feature can only be limited to its subgroups. However, this project will choose three PWP antecedent and current precipitation and two antecedent precipitation as the model structure. Normalization or data reduction is applied to all training and test data sets and is set only in the range from 0 to 1.
RMSE is intended to measure the difference between the observed PWP values and the predicted PWP. As for R2, it can be defined as the predicted proportion of the variance in the dependent variable using the independent variable. Approaching R2 to 1 value is favorable in this scope of the project as it will indicate that the developed model has performed well in predicting PWP.
The last phase, which completes the entire project based on the findings and analyzes carried out in the previous phase. Development findings and analysis Submission of progress report Completion of study Pre-SEDEX Submission of final draft report.
RESULT AND DISCUSSION
Comparison between Training Datasets and Testing Datasets
Statistical data analysis has been performed on both PWP and rainfall data, and the statistics can be viewed below. N=Number of data, Min=minimum value of data, Max=maximum value of data, SM=sample mean, SV=sample variance, SD=standard deviation.
Model Performance Evaluation
Figure 7 has shown the comparison of developed model to predict the pore water pressure responses to rainfall. Blue color plot represents the predicted PWP as the orange color represents the observed PWP. Meanwhile, the line graph should indicate the rainfall intensity in relation to the observed PWP and the predicted PWP.
Based on the graph plot, it showed that the predicted PWP mimicked the observed PWP as the plot pattern is identical to each other. This has shown the ability of the model to produce the forecast that can give almost similar reading compared to the observed data. It can also be observed that the pattern of observed and predicted PWP is fluctuating.
It has been found that the pore water pressure in the soil will increase after a high rainfall intensity. While the pore water pressure in the soil will decrease after a number of periods of low rainfall intensity. The fluctuation pattern of the pore water pressure has provided evidence to claim that the rainfall can cause the variability of the pore water pressure.
To explain more, this occurrence could possibly be due to the infiltration of rainfall into the ground, allowing the water to fill the void and then exert pressure outward on the ground. Based on Figure 8, the scatter plot is shown between observed PWP and predicted PWP. Referring to the graph of Figure 8, it is also able to evaluate the performance of the forecasting model in predicting the pore water pressure response to rainfall intensity.
The value of the obtained R2 is 0.93, which means that the predicted obtained PWP is almost equal to the observed PWP. It is better to bring the value of RMSE closer to 0 as this may indicate that the model can produce smaller differences compared to the observed PWP. So it has shown that a good result in R2 does not necessarily give a good RMSE value.
CONCLUSION AND RECOMMENDATION
Mustafa MR, Rezaur RB, Saiedi S., Rahardjo H., and Isa M.H (2013) Evaluation of MLP-ANN Training Algorithms for Modeling Soil-Water Pore Pressure Responses to Rainfall. Levenberg-Marqurdt Learning Neural Network for Time-Different Hpa Adaptive Predistortion with Memory in Ofdm Systems.
