Misgana K. Muleta

Improving Model Performance Using Season-Based Evaluation

Computer models have become vital decision-making tools in many areas of science and engineering including water resources. However, models should be properly evaluated before use to improve the likelihood of making sound decisions based on their results. The model evaluation technique practiced today in hydrology assumes that model parameters are season insensitive and attempts to identify “optimal” values that would describe watershed behavior during dry and wet seasons. This assumption could compromise accuracy of model predictions. This study demonstrates performance improvement that would be achieved when a season-based model evaluation approach is pursued. A global sensitivity analysis (SA) model has been used to investigate seasonal sensitivity of streamflow parameters of a watershed simulation model on the headwaters of the Little River Watershed, one of the United States Department of Agriculture’s experimental watersheds. Two separate analyses have been performed: the conventional approach in wh...

Watershed Management Technique to Control Sediment Yield in Agriculturally Dominated Areas

Abstract Non-point source pollution is recognized internationally as a critical environmental problem. In Illinois, soil erosion from agricultural lands is the major source of such pollution. The erosion process, which has been accelerated by human activity, tends to reduce crop productivity and leads to subsequent problems from deposition on farmlands and in water bodies. Comprehensive watershed management, however, can be used to protect these natural resources. In this study, a discrete time optimal control methodology and computational model are developed for determining land use and management alternatives that minimize sediment yield from agriculturally-dominated watersheds. The solution methodology is based on an interface between a genetic algorithm and the U.S. Department of Agricultures Soil and Water Assessment Tool. Model analyses are performed on a farm field basis to allow capture of different, local stakeholder perspectives, and crop management alternatives are based on a three-year rotation pattern. The decision support tool is applied to the Big Creek watershed located in the Cache River basin of Southern Illinois. The application demonstrates that the methodology is a valuable tool in advancing comprehensive watershed management. The study represents part of an ongoing research effort to develop an even more comprehensive decision support tool that uses multi-criteria evaluation to address social, economic, and hydrologic issues for integrative watershed management.

Analysis and Calibration of RDII and Design of Sewer Collection Systems

Excessive wet weather flow resulting from rainfall-derived inflow and infiltration (RDII) is a major source of sanitary sewer overflows (SSOs). SSOs pose serious problem to the public and the environment by causing back up into basements and sewer overflows to streets and rivers. Control of sewer overflows is, therefore, vital to reducing risks to public health and protecting the environment from water pollution. Computer modeling of sewer collection systems plays an important role in determining sound and economical remedial solutions that reduce RDII, improve system integrity, reliability and performance, and avoid overflows. This paper presents a rigorous and efficient three-step optimization methodology for use in solving the sewer overflow problem. The first step analyzes measured sewer flow and rainfall data and decomposes the flow data into dryweather flow and wet-weather flow components. The second step computes the optimal RTK parameters of the tri-triangular unit hydrograph that is commonly used to model RDII into the sewer collection system. The optimal RTK parameters are calibrated with genetic algorithm so that the simulated RDII flows closely match the RDII time series generated by decomposing the measured flow data. In the final step, the calibrated model is then used with genetic algorithm to design cost-effective solutions for existing SSO problems. Design parameters can include any combinations of pipe size, storage, slope, and pumping. The proposed wet-weather flow decomposition, optimal calibration, and optimal design models are demonstrated using an example sewer collection system. The methodology seems a good alternative to other methods proposed in the literature and should prove useful for engineers and planners that are involved in mitigating complex SSO problems.

Multiobjective Optimization for Optimal Design of Urban Drainage Systems

Control of sewer overflows, the leading cause of water pollution in the nation’s water bodies, is vital to reducing risks to public health and protecting the environment. The most common solutions for mitigating sewer overflows include adding storage volume, increasing conduit capacity, expanding pumping capacity, and implementation of real time operational controls to more effectively utilize existing system storage. Obviously, comprehensive modeling and analysis of these sewer systems becomes necessary for developing sound cost-effective and reliable solutions for enhancing system integrity and performance to convey sewer flows without causing overflows. However, identification of the optimal remedial solution that effectively circumvents overflow problems with the least expenditure is a daunting task. The current practice involves a tedious trial-and-error evaluation procedure that seldom leads to the most effective or most economical solutions. Another emerging design approach utilizes single objective optimization that identifies the solution that best satisfies a predefined criterion. The performance criterion used with single objective optimization subjectively lumps the economics objective with metrics that measure effectiveness of the remedial solution from the perspective of avoiding overflows (e.g., minimizing the number of flooding events or reducing the flooding volume). Consequently, the design solution identified using single objective optimization depends on the weights subjectively placed on the two incommensurable and conflicting objectives, and may not represent the global optimal solution. A preferable approach is to seek tradeoff solutions commonly referred to as non-dominated solutions or Pareto-optimal solutions. The methodology proposed here links an extended version of the EPA SWMM 5 model, a comprehensive drainage network simulator, with NSGA-II, an evolutionary multiobjective optimization method with a proven history of identifying Pareto-optimal solutions for a wide range of engineering problems. The method should prove useful to any wastewater utility attempting to improve system integrity, reliability and performance and optimize its capital improvement program.

Using Genetic Algorithms and SWAT to Minimize Sediment Yield From an Agriculturally Dominated Watershed

Non-point source pollution is well recognized as one of the most critical environmental hazards of modern times. In Illinois, non-point source pollution is the major cause of water quality problems, and soil erosion from agricultural lands is the major source of such pollution. Accelerated by anthropogenic activities, soil erosion reduces crop productivity and leads to subsequent problems from deposition on farmlands and in water bodies. Watershed management, however, promotes protection and restoration of these natural resources while allowing for sustainable economic growth and development. In this study a discrete time optimal control methodology and computational model are developed for determining land use and management alternatives that minimize sediment yield from agriculturally dominated watersheds. The methodology is based on an interface between a genetic algorithm and a U.S. Department of Agriculture watershed model known as Soil and Water Assessment Tool (SWAT). The original structure of the SWAT model is preserved and modifications are embedded for computational efficiency. The analysis is based on a farm field level to capture the perspectives of different stakeholders. The model thus supports Illinois EPA’s plan of developing a program based on enabling and empowering local stakeholders to take charge of the fate of their watershed. Management alternatives available for all land uses modeled by SWAT are developed considering rotation patterns of three years. The decision support tool is applied to Big Creek sub-watershed in the Cache River watershed, located in Southern Illinois. Big Creek subwatershed has been sighted by the Illinois EPA for excessive sediment and nutrient loadings and has been targeted by the Illinois Pilot Watershed Program. This research is part of an ongoing effort to develop a comprehensive decision support tool that uses multi-criteria evaluation to address social, economic and hydrologic issues for integrative watershed management.

Optimal Design of Urban Drainage Systems using Genetic Algorithms

Control of sewer overflows is vital to reducing risks to public health and protecting the environment from water pollution. Sewer overflows are a leading cause…

An Innovative Geocentric Decision Support Solution to Comprehensive Planning, Design, Operation, and Management of Urban Drainage Systems

Geographic Information System (GIS) is quickly becoming a critical component to develop and sustain asset management for today’s wastewater utilities as most of their data is geographically referenced. This technology offers sophisticated data management and spatial analysis capabilities that can greatly improve and facilitate urban drainage infrastructure modeling and analysis applications. This paper presents a comprehensive GIS-based decision support system that integrates several technologies for use in the effective management of urban stormwater collection systems. It explicitly integrates ESRI ArcGIS geospatial model with advanced hydrologic, hydraulic, and water quality simulation algorithms, nature-based global optimization techniques including genetic algorithms for design and calibration of stormwater management models, automated dry weather flow generation and allocation, and automated subcatchment delineation and parameter extraction tools to address every facet of urban drainage infrastructure management. The geocentric interface allows seamless communication among the various modules. The resulting decision support system effortlessly reads GIS datasets, extracts necessary modeling information, and automatically constructs, loads, designs, calibrates, analyzes and optimizes a representative urban drainage model considering hydrologic and hydraulic performance requirements. It also makes it easy to run, simulate and compare various modeling scenarios, identify system deficiencies, and determine cost-effective physical and operational improvements to achieve optimum performance and regulatory compliance. These combined capabilities provide favorable geospatial environment to assist wastewater utilities in planning, designing, and operating reliable systems and in optimizing their capital improvement programs.

An Explicit Conduit Storage Synthesis Algorithm for Solving Decoupled Forcemain Networks

A typical collection system may include many gravity mains connected by pump stations pushing flow into a complex network of pressurized conduits or forcemains…

Comprehensive ArcGIS-Based Urban Drainage Modeling for Decision Support

Computer simulation models of urban drainage systems represent the most effective and viable means for evaluating system response to various management strateg…

A Multiobjective SDSS for Management of Urbanizing Watersheds: The Case of the Lower Kaskaskia Basin, Illinois

The conversion of natural and agriculturally dominated watersheds to industrial, commercial and residential developments leads to a cascade of adjustments in runoff quantity and stream quality at locations further downstream. The use of sophisticated hydrologic simulation models and Geographic Information Systems (GIS) has become the standard for evaluating these impacts of urban sprawl on water resources systems. Simulation and GIS models alone, however, are incapable of directly revealing optimal land development patterns that meet specified objectives. This paper describes the development of a multi-objective Spatial Decision Support System (SDSS) designed to overcome this limitation. The SDSS is created by integrating the U.S. Department of Agriculture’s Soil and Water Assessment Tool (SWAT) for comprehensive hydrologic simulation, a GIS for generating input and visualizing output, and a genetic algorithm (GA) for identifying weighted, optimal land use patterns. In addition to the GA, future research will involve the integration of a second search mechanism, the artificial life algorithm, to verify optimal results. The optimal landscape is that which minimizes sediment yield in subsequent streams, while simultaneously maximizing approximate anticipated profit from urban development. The SDSS could be a useful visualization tool for land use managers and watershed management institutions in planning new developments. The SDSS has been tested on the Lower Kaskaskia watershed, located in the Metro East area of southwestern Illinois. Evidenced by a historical survey of population growth and hydrologic and water quality variability, this basin is an example of a watershed that is undergoing extensive water resources changes as a result of urbanization. An investigation of watershed planning activities and stakeholder groups in the watershed has also been undertaken. Meetings with these individuals have allowed direct dissemination of the research to affected groups and have been useful for generating feedback on future work and model modifications. Introduction and Background As increasing urban development become ever more apparent, the need to understand and control the environmental effects of urban sprawl intensifies. As a result, decision makers involved in land management are in need of specialized tools for evaluating optimal land development strategies. For example, a typical problem facing decision makers is the evaluation of land use patterns that Minimize → The adverse effects on water quality and quantity caused by urbanization, and; (-1) × economic growth and profit to be earned through urbanization. Subject To → (i) Physical, chemical and biological laws governing watershed hydrology and ecology; and (ii) Realistic bound constraints on the feasible land development. Since its introduction in the early 1960’s, Geographic Information Systems (GIS) have been increasingly popular for studying a variety of similar spatial problems (DeMers, 2000). A GIS is designed to store, retrieve, manipulate, analyze, and display spatial data. The disadvantage to using GIS in this manner is that, alone, it does not have the capability to perform complex process modeling. In order to overcome this limitation, many modeling systems have evolved and coupled GIS with process (i.e., environmental or hydrologic) simulation models to work together as one integrated system. Because GIS has the ability to handle both temporal and attribute data, it has the capability to increase the accuracy and quality of simulation modeling. Moreover, the principle goal in most modeling is to be able to handle large amounts of geographic data, a task which can be performed easily by GIS (Nyerges, 1993). A GIS that easily integrates models has been called a “Geographic Information Modeling System” (Dangermond, 1987). The subsystems of a coupled system include input/capture, management, manipulation/analysis, and output/display. The words loose and tight have been used to describe coupled environments and refer to the compatibility of data constructs of the subsystems with the software operations used to process them (Nyerges, 1993). A loosely coupled system is a system in which a simple data transfer takes place from one system to another. An example of a loosely coupled system is joining, or coupling, two separate GIS modules in order to transfer data between the two. A GIS and model are said to be tightly coupled when they rely on an integrated data management system. The tightest coupling is called an embedded system where both the GIS and the hydrologic model rely on the same data source. As can be expected, this is very complicated, and due to the complexity of an embedded system, they are financially and computationally expensive to develop. Furthermore, the user is often left with a system that is very case specific and is most likely incompatible to problems of different disciplines. For this reason looser, more generic, systems are much more common. Although coupled GIS/hydrologic modeling systems greatly improve capabilities for analyzing spatial problems, including the evaluation of environmental effects of urban sprawl, they are incapable of directly determining optimal land development strategies. In essence, their results begin to lose importance when viewed in light of objectives, sometimes multiple in number and conflicting in nature, to be achieved. As a result, researchers are increasingly making use of integrated software systems called Decision Support Systems (DSS) and Spatial Decision Support Systems (SDSS). A DSS acts as a framework for operations research and data management systems (Sprague, 1980). An SDSS further integrates database management systems with analytical models (i.e., hydrologic models), graphical display (i.e., map layouts) and tabular reporting capabilities (i.e., ASCII files, spreadsheets), and the expert knowledge of the decision maker (i.e. land use manager) (Densham, 1991). Multi-criteria evaluation (MCE) techniques, also referred to as multi-criteria analysis or MCA, can be integrated within an SDSS to analyze the complex trade-offs between quantitative choice alternatives with different socio-economic impacts. The primary objective of MCE techniques is to investigate a number of possibilities in the light of multiple criteria and conflicting objectives (Voogd, 1983; Carver, 1991). A particular class of methods that are well suited for an SDSS is the multiple criteria decision-making (MCDM) method, in which MCE techniques are a component. The general objective of MCDM is to help the decision maker select the best alternative based on a number of possible alternatives in the presence of multiple-choice criteria (Jankowski, 1995). Another important aspect of an SDSS is the operations research, or optimization, component. Here, the purpose of the optimization is to reveal better decisions by starting with an initial concept and using information gained through previous iterations to improve upon the idea. A genetic algorithm (GA), although heuristic in nature, could be used in this sense. GAs follow the principles of Darwinian natural selection, where only the strong individuals survive. Starting with an initial population, the chromosomes (i.e., alternative decisions) evolve through a series of operations, including ranking according to fitness, mating, crossover, and mutation (Haupt and Haupt, 1998). For this project, a tightly coupled model and a GA are integrated within an SDSS. The resulting modeling system has been applied to the Lower Kaskaskia watershed that is located in southwest Illinois, near St. Louis, in an area called Metro East. The goal of the model is to identify land use patterns that minimize sediment yield, while maximizing the approximate anticipated economic profit to be earned from urbanization. Evaluation of Urban Sprawl and Its Effects An attempt was first made to conduct a historical population survey for the watershed to determine the extent of urban sprawl near the Metro East area in recent years. This survey was completed using population census data from the U.S. Census Bureau and was conducted on a census tract scale. When originally delineated, census tracts were homogeneous with respect to population characteristics, economic status, and living conditions. They are maintained with the intent of making comparisons between subsequent census surveys. Some tracts in densley populated areas required subdivision due to the increase of population in the area (USCB, 2000). A GIS overlay technique was used to determine which census tracts fell within the Lower Kaskaskia watershed, and data was subsequently collected for each tract. The data from the 2000 census was very thorough, but the availabiltiy of data decreased for each preceding census, and census tract data was only available from 1970 to 2000. In addition, the comparison of the 2000 census and 1990 census proved to be difficult due to the fact that some census tracts were redelineated in 2000. Finally, the source for 1970 and 1980 data did not include some data for rural areas. Based on available data, the census tracts were compared using the percent change in total population that occurred within the corresponding decade. The results of the survey, illustrated in Figure 1, generally indicate an increase in population, particularly in areas that serve as subburban or bedroom communities to St. Louis. In addition to the population survey, a separate review of historical changes in peak flow and water quality at various stream locations has confirmed a general increase in runoff quantity and sediment loads in the watershed. 1970-198