Date of Graduation
Statler College of Engineering and Mineral Resources
Lane Department of Computer Science and Electrical Engineering
In studying dynamic and complex processes which are influenced by a system of inter-connected driving variables, it is crucial to apply models that can learn the complexity of the interactions. Land transformation is one of such complex processes, prediction of which can help to mitigate severe climate situations and improve the resiliency of communities. In this study, a multi-spectral set of data cubes is used to capture various characteristics of a geographic region. Based on the data cube, a feature space is constructed using socio-economic attributes, terrain characteristics, and landscape traits of the study region. Two-dimensional and three-dimensional convolutional neural networks (CNNs) and ensemble methods are then applied on this feature space to investigate model performance and improve the robustness of the models. This research demonstrates an application of evidence fusion at feature, decision and score levels, and applies ensemble random forests for post processing. Performance is assessed using the Dice coefficient, Receiver Operating Characteristic (ROC) curves, data visualization, and running time. This study shows that the implementation of ensemble models improved the accuracy of the models in terms of model stability, precision and recall, and dice coefficient.
pourmohammadi, Pariya, "Ensemble Encoder-Decoder Models for Predicting Land Transformation" (2021). Graduate Theses, Dissertations, and Problem Reports. 8251.