Neural networks as a commonly used machine learning algorithms, such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), have been extensively used in GIScience domain to explore the nonlinear geographic phenomena. However, there are a few studies that investigate the parameter settings of neural networks in GIScience. Moreover, the model performance of neural networks often depends on the parameter setting for a given dataset. Meanwhile, adjusting the parameter configuration of neural networks will increase the overall running time. Therefore, an automated approach is necessary for addressing these limitations in current studies. In this dissertation, I proposed an automated spatially explicit hyperparameter optimization approach to identify optimal or near-optimal parameter settings for neural networks and accelerate the search process through both model and computing levels. I used two spatial prediction models in this dissertation to examine the utilities of spatially explicit hyperparameter optimization. The results demonstrate that the approach proposed in this dissertation improves the computing performance at model and computing levels and addresses the challenge of finding optimal parameter settings for neural networks in the GIScience field.
