abstract: Drawn from a trio of manuscripts, this dissertation evaluates the sustainability contributions and implications of deploying underutilized spaces for alternative uses at multiple scales: urban, regional and continental. The first paper considers the use of underutilized spaces at the urban scale for urban agriculture (UA) to meet local sustainability goals in Phoenix, Arizona. Through a data-driven analysis, it demonstrates UA can meet 90% of annual demand for fresh produce, supply local produce in all food deserts, reduce areas underserved by public parks by 60%, and displace >50,000 tons of carbon-dioxide emissions from buildings. The second paper considers marginal agricultural land use for bioenergy crop cultivation to meet future liquid fuels demand from cellulosic biofuels sustainably and profitably. At a wholesale fuel price of $4 gallons-of-gasoline-equivalent, 30 to 90.7 billion gallons of cellulosic biofuels can be supplied by converting 22 to 79.3 million hectares of marginal lands in the Eastern United States (U.S.). Displacing marginal croplands (9.4-13.7 million hectares) reduces stress on water resources by preserving soil moisture. This displacement is comparable to existing land use for first-generation biofuels, limiting food supply impacts. Coupled modeling reveals positive hydroclimate feedback on bioenergy crop yields that moderates the land footprint. The third paper examines the sustainability implications of expanding use of marginal lands for corn cultivation in the Western Corn Belt, a commercially important and environmentally sensitive U.S. region. Corn cultivation on lower quality lands, which tend to overlap with marginal agricultural lands, is shown to be nearly three times more sensitive to changes in crop prices. Therefore, corn cultivation disproportionately expanded into these lands following price spikes. Underutilized spaces can contribute towards sustainability at small and large scales in a complementary fashion. While supplying fresh produce locally and delivering other benefits in terms of energy use and public health, UA can also reduce pressures on croplands and complement non-urban food production. This complementarity can help diversify agricultural land use for meeting other goals, like supplying biofuels. However, understanding the role of market forces and economic linkages is critical to anticipate any unintended consequences due to such re-organization of land use. / Dissertation/Thesis / Doctoral Dissertation Geography 2020

This thesis offers a thorough investigation into the application of machine learning algorithms for predicting the presence of vehicles in a traffic setting. The research primarily focuses on enhancing vehicle simulation by employing data-driven traffic prediction methods. The study approaches the problem as a binary classification task. Various supervised learning algorithms, including Random Forest (RF), Gradient Boosting (GB), Support Vector Machine (SVM), and Logistic Regression (LogReg) were evaluated and tested. The thesis encompasses six distinct implementations, each involving different combinations of algorithms, feature engineering, hyperparameter tuning, and data splitting. The performance of each model was assessed using metrics such as accuracy, precision, recall, and F1-score, and visualizations like ROC-AUC curves were used to gain insights into their discrimination capabilities. While the RF model achieved the highest accuracy at 97%, the AUC score of Combination 2 (RF+GB) suggests that this ensemble model could strike a better balance between high accuracy (86%) and effective class separation (99%). Ultimately, the study identifies an ensemble model as the preferred choice, leading to significant improvements in prediction accuracy. The research also explores working on the problem as a time-series prediction task, exploring the use of Long Short-Term Memory (LSTM) and Auto-Regressive Integrated Moving Average (Auto-ARIMA) models. However, we found that this approach was impractical due to the dataset’s discrete and non-sequential nature. This research contributes to the advancement of vehicle simulation and traffic forecasting, demonstrating the potential of machine learning in addressing complex real-world scenarios.

The interest towrds machine learning applied to turbulence has experienced a fast-paced growth in the last years. Thanks to deep-learning algorithms, flow-control stratigies have been designed, as well as tools to model and reproduce the most relevant turbulent features. In particular, the success of recurrent neural networks (RNNs) has been demonstrated in many recent studies and applications. The main objective of this project is to assess the capability of these networks to reproduce the temporal evolution of a minimal turbulent channel flow. We first obtain a data-driven model based on a modal decomposition in the Fourier domain (FFT-POD) on the time series sampled from the flow. This particular case of turbulent flow allows us to accurately simulate the most relevant coherent structures close to the wall. Long-short-term-memory (LSTM) networks and a Koopman-based framework (KNF) are trained to predict the temporal dynamics of the minimal channel flow modes. Tests with different configurations highlight the limits of the KNF method compared to the LSTM, given the complexity of the data-driven model. Long-term prediction for LSTM show excellent agreement from the statistical point of view, with errors below 2% for the best models. Furthermore, the analysis of the chaotic behaviour thorugh the use of the Lyapunov exponent and of the dynamic behaviour through Pointcaré maps emphasizes the ability of LSTM to reproduce the nature of turbulence. Alternative reduced-order models (ROMS), based on the identification of different turbulent structures, are explored and they continue to show a good potential in predicting the temporal dynamics of the minimal channel.