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Chapter
Introduction
In the past decades, with the increasing volume of spatial data and development of cutting-edge techniques, several spatial models have been created to investigate complex spatial phenomena and explore spatial...
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Chapter
Methodology
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Chapter
Study II. Spatially Explicit Hyperparameter Optimization of Neural Networks Accelerated Using High-Performance Computing
The major purpose of this chapter is to demonstrate the performance of the automated spatially explicit hyperparameter optimization (corresponding to objective 2). More specifically, I use the same case study ...
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Chapter
Conclusion
The primary purpose of this book is to propose a framework that could apply to machine learning algorithm-based spatial models with consideration of spatial features. The integration of machine learning and GI...
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Chapter
Literature Review
This literature review covers four topics, including artificial neural networks, hyperparameter optimization, cyberinfrastructure and high-performance and parallel computing, and evolutionary algorithms.
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Chapter
Study I. Hyperparameter Optimization of Neural Network-Driven Spatial Models Accelerated Using Cyber-Enabled High-Performance Computing
In this chapter, the major purpose is to examine the feasibility and necessity of spatially explicit hyperparameter optimization and to evaluate the performance of hyperparameter optimization in neural network...
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Chapter
Study III. An Integration of Spatially Explicit Hyperparameter Optimization with Convolutional Neural Networks-Based Spatial Models
In this chapter, the study shows the performance of an integration of spatially explicit hyperparameter optimization.