Multiobjective Design and Innovization of Robust Stormwater Management Plans

In the United States, states are federally mandated to develop watershed management plans to mitigate pollution from increased impervious surfaces due to land development such as buildings, roadways, and parking lots. These plans require a major investment in water retention infrastructure, known as structural Best Management Practices (BMPs). However, the discovery of BMP configurations that simultaneously minimize implementation cost and pollutant load is a complex problem. While not required by law, an additional challenge is to find plans that not only meet current pollutant load targets, but also take into consideration anticipated changes in future precipitation patterns due to climate change. In this dissertation, a multi-scale, multiobjective optimization method is presented to tackle these three objectives. The method is demonstrated on the Bartlett Brook mixed-used impaired watershed in South Burlington, VT. New contributions of this work include: (A) A method for encouraging uniformity of spacing along the non-dominated front in multiobjective evolutionary optimization. This method is implemented in multiobjective differential evolution, is validated on standard benchmark biobjective problems, and is shown to outperform existing methods. (B) A procedure to use GIS data to estimate maximum feasible BMP locations and sizes in subwatersheds. (C) A multi-scale decomposition of the watershed management problem that precalculates the optimal cost BMP configuration across the entire range of possible treatment levels within each subwatershed. This one-time pre-computation greatly reduces computation during the evolutionary optimization and enables formulation of the problem as real-valued biobjective global optimization, thus permitting use of multiobjective differential evolution. (D) Discovery of a computationally efficient surrogate for sediment load. This surrogate is validated on nine real watersheds with different characteristics and is used in the initial stages of the evolutionary optimization to further reduce the computational burden. (E) A lexicographic approach for incorporating the third objective of finding non-dominated solutions that are also robust to climate change. (F) New visualization methods for discovering design principles from dominated solutions. These visualization methods are first demonstrated on simple truss and beam design problems and then used to provide insights into the design of complex watershed management plans. It is shown how applying these visualization methods to sensitivity data can help one discover solutions that are robust to uncertain forcing conditions. In particular, the visualization method is applied to discover new design principles that may make watershed management plans more robust to climate change

On The Application Of Computational Modeling To Complex Food Systems Issues

Transdisciplinary food systems research aims to merge insights from multiple fields, often revealing confounding, complex interactions. Computational modeling offers a means to discover patterns and formulate novel solutions to such systems-level problems. The best models serve as hubs—or boundary objects—which ground and unify a collaborative, iterative, and transdisciplinary process of stakeholder engagement. This dissertation demonstrates the application of agent-based modeling, network analytics, and evolutionary computational optimization to the pressing food systems problem areas of livestock epidemiology and global food security. It is comprised of a methodological introduction, an executive summary, three journal-article formatted chapters, and an overarching discussion section. Chapter One employs an agent-based computer model (RUSH-PNBM v.1.1) developed to study the potential impact of the trend toward increased producer specialization on resilience to catastrophic epidemics within livestock production chains. In each run, an infection is introduced and may spread according to probabilities associated with the various modes of contact between hog producer, feed mill, and slaughter plant agents. Experimental data reveal that more-specialized systems are vulnerable to outbreaks at lower spatial densities, have more abrupt percolation transitions, and are characterized by less-predictable outcomes; suggesting that reworking network structures may represent a viable means to increase biosecurity. Chapter Two uses a calibrated, spatially-explicit version of RUSH-PNBM (v.1.2) to model the hog production chains within three U.S. states. Key metrics are calculated after each run, some of which pertain to overall network structures, while others describe each actor’s positionality within the network. A genetic programming algorithm is then employed to search for mathematical relationships between multiple individual indicators that effectively predict each node’s vulnerability. This “meta-metric” approach could be applied to aid livestock epidemiologists in the targeting of biosecurity interventions and may also be useful to study a wide range of complex network phenomena. Chapter Three focuses on food insecurity resulting from the projected gap between global food supply and demand over the coming decades. While no single solution has been identified, scholars suggest that investments into multiple interventions may stack together to solve the problem. However, formulating an effective plan of action requires knowledge about the level of change resulting from a given investment into each wedge, the time before that effect unfolds, the expected baseline change, and the maximum possible level of change. This chapter details an evolutionary-computational algorithm to optimize investment schedules according to the twin goals of maximizing global food security and minimizing cost. Future work will involve parameterizing the model through an expert informant advisory process to develop the existing framework into a practicable food policy decision-support tool