Spatially Explicit Hyperparameter Optimization for Neural Networks

Neural networks are commonly used machine learning algorithms in GIScience, but there are limited studies on parameter settings and optimizing their performance. This book proposes an automated spatially explicit hyperparameter optimization approach to identify optimal parameter settings for neural networks, improving computing performance and addressing limitations in current studies. It is written for researchers in GIScience and social science subjects.

Format: Paperback / softback
Length: 108 pages
Publication date: 20 October 2022
Publisher: Springer Verlag, Singapore

Neural networks, widely employed in machine learning, have found extensive application in the realm of GIScience to unravel the intricate and nonlinear characteristics of geographic phenomena. While these algorithms, including artificial neural networks (ANNs) and convolutional neural networks (CNNs), have demonstrated their effectiveness in analyzing diverse spatial data, there exists a limited body of research dedicated to exploring the parameter settings of neural networks within the GIScience context. Furthermore, the performance of neural networks often hinges on the judicious selection of parameters for a specific dataset. However, the process of adjusting these parameters can significantly prolong the running time of the models. Consequently, there is a pressing need for an automated approach to address these limitations in current studies.

In response to this challenge, this book presents an automated spatially explicit hyperparameter optimization approach aimed at identifying optimal or near-optimal parameter settings for neural networks in the GIScience field. By leveraging advanced optimization techniques, the approach aims to enhance the computing performance at both the model and computing levels. This book is specifically designed to cater to researchers and practitioners in the GIScience domain as well as social science subjects who are interested in leveraging the power of neural networks for spatial data analysis.

The book begins by introducing the fundamental concepts and principles of neural networks and their applications in GIScience. It then delves into the challenges associated with parameter optimization, including the need for automated approaches, the impact of parameter settings on model performance, and the trade-off between computational efficiency and model accuracy. The book proceeds to present the proposed automated hyperparameter optimization approach, which encompasses a range of techniques such as genetic algorithms, Bayesian optimization, and gradient descent optimization.

Each technique is carefully explained and illustrated with examples and case studies drawn from the GIScience domain. The authors demonstrate how these techniques can be applied to different types of neural networks, including ANNs and CNNs, and how they can be tailored to specific datasets and problem scenarios. The book also discusses the importance of validating and evaluating the performance of optimized neural networks, including measures such as accuracy, precision, recall, and F1 score.

Furthermore, the book explores the potential applications of the automated hyperparameter optimization approach in various GIScience fields, such as land use/land cover mapping, urban analysis, climate change detection, and natural disaster response. The authors showcase real-world examples and case studies that demonstrate the effectiveness of the approach in improving the accuracy and efficiency of neural network models.

In conclusion, this book offers a comprehensive and practical guide to automated spatially explicit hyperparameter optimization for neural networks in GIScience. By providing a detailed understanding of the optimization techniques, the book empowers researchers and practitioners to enhance the performance and efficiency of neural network models, enabling them to make more informed decisions based on spatial data analysis. Whether you are a GIScience researcher, practitioner, or social science subject, this book is an invaluable resource for unlocking the full potential of neural networks in your work.

Weight: 209g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811654015
Edition number: 1st ed. 2021