Marcio Giacomoni

Complex adaptive systems approach to simulate the sustainability of water resources and urbanization

AbstractUrban water resources should be managed to meet conflicting demands for environmental health, economic prosperity, and social equity for present and future generations. While the sustainability of water resources can depend on dynamic interactions among natural, social, and infrastructure systems, typical water resource planning and management approaches are based on methodologies that ignore feedbacks and adaptations among these systems. This research develops and demonstrates a new complex adaptive systems approach to model the dynamic interactions among population growth, land-use change, the hydrologic cycle, residential water use, and interbasin transfers. Agent-based and cellular automaton models, representing consumers and policymakers who make land- and water-use decisions, are coupled with hydrologic models. The framework is applied for an illustrative case study to simulate urbanization and the water supply system over a long-term planning horizon. Results indicate that interactions amon...

Hydrologic Footprint Residence: Environmentally Friendly Criteria for Best Management Practices

The natural hydrologic flow regime is altered by urbanization, which can be mitigated through best management practices (BMPs) or low impact development (LID). Typically, the effectiveness of different management scenarios is tested by comparing post- and predevelopment instantaneous peak flows. This approach, however, does not capture the extent of hydrologic change and the effect on downstream communities. A new hydrologic sustainability metric is presented here to quantify the impact of urbanization on downstream water bodies on the basis of the inundation dynamics of the flow regime. The hydrologic footprint residence (HFR) is designed to capture both temporal and spatial hydrological changes to an event-based flow regime by calculating the inundated areas and duration of a flood. The HFR is demonstrated for a hypothetical watershed and a watershed on the Texas A&M University Campus, located in College Station, Texas. For the campus watershed, three design storms (2-, 10-, and 100-year) and a set of h...

Complex Adaptive Modeling Framework for Evaluating Adaptive Demand Management for Urban Water Resources Sustainability

AbstractNew water resources management methodologies are needed to address increasing demands and future uncertainty for urban water resources. Adaptive water demand management strategies provide an approach to improve the efficiency of water system operations and meet water demands by adapting flexibility to increasing stresses, such as droughts. This study simulates adaptive water demand management through the development of a complex adaptive system modeling framework, which couples cellular automata modeling, agent-based modeling, and hydrologic modeling to simulate land-use change, consumer behaviors, management decisions, the rainfall-runoff process, and reservoir storage. The model is applied to simulate the effect of demand management strategies on reductions in municipal water demands and on the sustained storage in a surface water supply reservoir. Historic and projected climate change hydroclimatic time series are used to assess the effectiveness of domestic water restrictions, including outdoo...

An evolutionary algorithm approach to generate distinct sets of non-dominated solutions for wicked problems

Many engineering design problems must optimize multiple objectives. While many objectives are explicit and can be mathematically modeled, some goals are subjective and cannot be included in a mathematical model of the optimization problem. A set of alternative non-dominated fronts that represent multiple optima for problem solution can be identified to provide insight about the decision space and to provide options and alternatives for decision-making. This paper presents a new algorithm, the Multi-objective Niching Co-evolutionary Algorithm (MNCA) that identifies distinct sets of non-dominated solutions which are maximally different in their decision vectors and are located in the same non-inferior regions of a Pareto front. MNCA is demonstrated to identify a set of non-dominated fronts with maximum difference in decision vectors for a set of real-valued problems.

Using the hydrologic footprint residence to evaluate Low Impact Development in urban areas.

Urbanization adversely impacts the health of a watershed and the receiving water body, as increased runoff volumes, velocities, and peak flows cause erosion, flooding, and degradation of ecosystem habitats. Low Impact Development (LID) strategies are used to mitigate the impacts of urbanization by reducing the runoff at the source and restoring the natural hydrologic flow regime. Rainwater harvesting, permeable pavements and green roofs may be placed in urban areas to mitigate the runoff generated from rooftops and parking lots. This study simulates and evaluates the placement of these LID strategies for an urban watershed on the Texas A&M University campus. A conventional metric, the peak flow, is used to evaluate the hydrologic performance of LID, in addition to the Hydrologic Footprint Residence (HFR), which is a new metric that captures the inundated areas and duration of floods in downstream reaches. The results indicate that HFR can be used to evaluate the hydrologic performance of LID as it captures both changes in runoff volumes and the duration of flooding to represent the impacts of urbanization.

Multi-objective evolutionary optimization and Monte Carlo simulation for placement of low impact development in the catchment scale

AbstractRestoring the hydrologic flow regime of urban areas by promoting infiltration, retention, and evapotranspiration on the site is one of the goals of low-impact development (LID). These goals...

Multiobjective Genetic Optimization Approach to Identify Pipe Segment Replacements and Inline Storages to Reduce Sanitary Sewer Overflows

Sanitary sewer overflows (SSOs) is the unintentional discharge of untreated sewage from the sanitary sewer system and pose serious risk to public health and to the environment. Rehabilitation plans to reduce SSOs involve increasing conveyance capacity and shaving peak flow using detention storages. Identifying the best location for rehabilitating the sanitary sewer network is a difficult task because of the great length of sanitary sewer systems. This study utilized single and multiobjective genetic algorithms (GAs) to design rehabilitation strategies for SSOs reduction in an existing sewer network. The Nondominated Sorting Genetic Algorithm II was linked to the EPA-SWMM to generate non-dominated sets of solutions that characterizes the tradeoffs between reduction in number of SSOs and cost (Case I), and the tradeoff between of volume of SSOs and cost (Case II). The results show that, when maximizing the reduction of number SSOs, the algorithm target first regions of the network with higher density of SSOs. When maximizing the reduction of volume of SSOs, the solutions prioritize the nodes with the largest overflow volumes. The tested approach provides a range of options to decision makers that seek to reduce or eliminate SSOs in an existing sanitary sewer system.

Enhancing the performance of a multiobjective evolutionary algorithm for sanitary sewer overflow reduction

AbstractThe application of multiobjective evolutionary algorithms (MOEAs) to sanitary sewer overflow (SSO) optimization problems typically requires multiple runs of a simulation model and can be ve...

A multi-objective niching co-evolutionary algorithm (MNCA) for identifying diverse sets of non-dominated solutions

Many engineering design problems must optimize multiple objectives. While many objectives are explicit and can be mathematically modeled, some goals are subjective and cannot be included in a mathematical model of the optimization problem. A set of alternative Pareto fronts that represent multiple optima for problem solution can be identified to provide insight about the decision space and to provide options and alternatives for decision-making. This paper presents the Multi-objective Niching Co-evolutionary Algorithm (MNCA) that identifies a set of Pareto-optimal solutions which are maximally different in their decision vectors and are located in the same non-inferior regions of the Pareto front. MNCA is demonstrated for a set of multi-modal multi-objective test problems to identify a set of Pareto fronts with maximum difference in decision vectors.

Application of a Multiobjective Genetic Algorithm to Reduce Wet Weather Sanitary Sewer Overflows and Surcharge

AbstractSanitary sewer overflow (SSO) is the discharge of wastewater from the collection network into the environment. SSOs are significant environmental and public health hazards. The EPA estimate...