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A comparison of various interpolation techniques for modeling and estimation of radon concentrations in Ohio
Contents
Abstract
Acknowledgements
Contents
List of Tables
List of Figures
List of Abbreviations
1. Introduction
1.1 Problem Statement
1.2 Research Approach
1.3 Organization of Thesis
2. Literature Review
2.1 Role of Interpolation
2.2 Prior Art
2.3 Review of Neural Network Techniques
3. Conventional Interpolation Techniques
3.1 Data Preparation
3.2 Comparative Performance Measures for Evaluating Interpolation Techniques15
3.2.1 Mean Absolute Error
3.2.2 Factor of Two
3.2.3 Root Mean Square Error
3.2.4 Fractional Bias
3.2.5 Normalized Mean Square Error
3.3 Advantages and Disadvantages of Conventional Interpolation Techniques
3.4 Results and Discussions
4. Neural Network Approaches
4.1 Artificial Neural Network Approach
4.1.1 Methodology
4.1.2 Results and Discussions
4.2 Knowledge Based Neural Network Approaches
4.2.1 Methodology
4.2.2 Results and Discussions
4.3 Correction-Based Neural Network Approach
4.3.1 Methodology
4.3.2 Results and Discussions
5. Support Vector Regression and Random Forest Regression 50
5.1 Support Vector Regression
5.1.1 Review of Support Vector Machines
5.1.2 Methodology
5.1.3 Results and Discussions
5.2 Random Forest Regression
5.2.1 Review of Random Forest Regression
5.2.2 Methodology
5.2.3 Results and Discussions
6. Conclusions and Future Work 64
6.1 Conclusions
6.2 Future Work
References
Appendix-A
A.1 Source code for K-fold Cross-Validation Data Preparation
A.2 Source code for Correction-Based ANN modeling approach
A.3 Source code for nu-SVR using LIBSVM package
A.4 R Software commands for executing ‘randomForest’ package
Appendix-B
B.1 Neuromodeler software Main Window
B.2 New Model Window in the Neuromodeler Software
B.3 Training Window of the Neuromodeler software
B.4 Window for modifying hidden neurons in the Neuromodeler software
B.5 Testing Window of the Neuromodeler Software
B.6 Export model to different platforms in the Neuromodeler Software Bài viết liên quan
