Spatially Explicit Hyperparameter Optimization for Neural Networks

Preface
Acknowledgements
Contents
List of Figures
List of Tables
1 Introduction
	1.1 Background
	1.2 Research Objectives
		1.2.1 Objective 1
		1.2.2 Objective 2
		1.2.3 Objective 3
	References
2 Literature Review
	2.1 Artificial Neural Network
	2.2 Hyperparameter Optimization
	2.3 Cyberinfrastructure and High-Performance and Parallel Computing
	2.4 Evolutionary Algorithms
	References
3 Methodology
	3.1 Overview
	3.2 Component 1—Automatic Search of Hyperparameters
	3.3 Component 2—Spatial Prediction of Hyperparameter Space
	3.4 Component 3—Acceleration of Hyperparameter Search
	References
4 Study I. Hyperparameter Optimization of Neural Network-Driven Spatial Models Accelerated Using Cyber-Enabled High-Performance Computing
	4.1 Introduction
	4.2 Literature Review
		4.2.1 Artificial Neural Networks
		4.2.2 Hyperparameter Optimization
	4.3 Study Area and Data
	4.4 Methodology
		4.4.1 Land Price Evaluation Model
		4.4.2 Hyperparameter Optimization
		4.4.3 Determining Optimal Sample Size
		4.4.4 Parallel Computing and Implementation
	4.5 Results
		4.5.1 Results of Grid Search and Random Search
		4.5.2 Prediction Performance of Hyperparameters
		4.5.3 Parallel Computing Performance
	4.6 Discussions
		4.6.1 Necessity of the Framework
		4.6.2 Feasibility of the Framework
		4.6.3 Computing Performance
	4.7 Conclusion
	References
5 Study II. Spatially Explicit Hyperparameter Optimization of Neural Networks Accelerated Using High-Performance Computing
	5.1 Introduction
	5.2 Study Area and Data
	5.3 Methodology
	5.4 Implementation
	5.5 Results
		5.5.1 Model Performance
		5.5.2 Prediction Performance of Hyperparameters
		5.5.3 Parallel Computing Performance
	5.6 Discussions
		5.6.1 The Prediction of Generalization Performance
		5.6.2 Computing Performance
	5.7 Conclusion
	References
6 Study III. An Integration of Spatially Explicit Hyperparameter Optimization with Convolutional Neural Networks-Based Spatial Models
	6.1 Introduction
	6.2 Hyperparameters of Convolutional Neural Networks
	6.3 Study Area and Data
	6.4 Experimental Design
		6.4.1 Setting of CNN Model
		6.4.2 CNN-Based Cellular Automata
		6.4.3 Implementation
	6.5 Results
		6.5.1 Accuracy Assessment
		6.5.2 Model Performance
		6.5.3 Generalization Performance of Hyperparameters
		6.5.4 Prediction Performance
		6.5.5 Parallel Computing Performance
	6.6 Discussions
		6.6.1 The Simulation Performance of CNN-CA Model
		6.6.2 Computing Performance
	6.7 Conclusion
	References
7 Conclusion