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| Author | : Jason Liam Dorn |
| Publisher | : |
| Total Pages | : 156 |
| Release | : 2004 |
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| ISBN | : |
Keywords: Watershed Management, Evolutionary Algorithms, Multiobjective Optimization, Modeling to Generate Alternatives, Decision Support System.
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| Total Pages | : |
| Release | : 2003 |
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Watershed management is a complex process involving multiple uses, diverse stakeholders, and a variety of computer-based hydrologic and hydraulic simulation models. Exploring for efficient solutions and making decisions about the best integrated management strategies to implement can be improved through the use of quantitative systems analytic techniques. In addition to identifying mathematically optimal solutions, these techniques should also be able to consider issues that may not be properly represented in the models or may be in conflict with one another. As the complexities of the system models grow, contemporary heuristic search methods, including evolutionary algorithms (EAs), are becoming increasingly common in quantitative analysis of such challenging decision-making problems. More research is needed to enhance and extend the capabilities of these newer search methods to meet the growing challenges. Further, these new systems analytic capabilities are best made accessible to practitioners through a generic computational framework that integrates the system simulation models with the suite of search techniques. Therefore, the purpose of this research is to develop new EA-based system analytic methods for addressing integrated watershed management problems and a computational framework within which their capabilities are enabled for watershed management applications. EA-based methods to generate good alternative solutions and for multiobjective optimization have been developed and tested, and their performances compare well with those of other procedures. These new methods were also demonstrated through successful applications to realistic problems in watershed management. These techniques were integrated into and implemented within a new computer-based decision support framework that supports the integration of the user's preferred watershed models, methods to perform uncertainty and/or sensitivity analyses thereon, and multiple state-of-the-art optimization.
| Author | : Can Ali Kuterdem |
| Publisher | : |
| Total Pages | : 25 |
| Release | : 2001 |
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| ISBN | : |
Keywords: watershed management, water quality, TMDL, decision support system, BASINS, HSPF, genetic algorithms, optimization.
| Author | : Oliver Schütze |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 504 |
| Release | : 2012-08-14 |
| Genre | : Technology & Engineering |
| ISBN | : 3642315194 |
This book comprises a selection of papers from the EVOLVE 2012 held in Mexico City, Mexico. The aim of the EVOLVE is to build a bridge between probability, set oriented numerics and evolutionary computing, as to identify new common and challenging research aspects. The conference is also intended to foster a growing interest for robust and efficient methods with a sound theoretical background. EVOLVE is intended to unify theory-inspired methods and cutting-edge techniques ensuring performance guarantee factors. By gathering researchers with different backgrounds, a unified view and vocabulary can emerge where the theoretical advancements may echo in different domains. Summarizing, the EVOLVE focuses on challenging aspects arising at the passage from theory to new paradigms and aims to provide a unified view while raising questions related to reliability, performance guarantees and modeling. The papers of the EVOLVE 2012 make a contribution to this goal.
| Author | : Prakash D. Kaini |
| Publisher | : |
| Total Pages | : 290 |
| Release | : 2010 |
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Water quality has been a major concern in the United States and elsewhere because of its impact on people's daily lives and on the environment. There are two main sources of water pollution: point sources and non-point sources, which are differentiated based on their mode of generation. Pollution generated from point sources has been effectively controlled by the implementation of the National Pollution Discharge Elimination System (NPDES) program. However, a large portion of the nation's water remains polluted, mainly due to non-point sources of pollution. Structural and non-structural Best Management Practices (BMPs) have been recognized as effective measures for controlling non-point sources of pollution. The objective of this research is to develop methodologies that can be used to design structural BMPs as measurements for controlling non-point sources of pollution (i.e. sediment and nutrients) on a larger spatial scale, that of a watershed.
| Author | : Omid Bozorg-Haddad |
| Publisher | : John Wiley & Sons |
| Total Pages | : 306 |
| Release | : 2017-10-09 |
| Genre | : Mathematics |
| ISBN | : 1119386993 |
A detailed review of a wide range of meta-heuristic and evolutionary algorithms in a systematic manner and how they relate to engineering optimization problems This book introduces the main metaheuristic algorithms and their applications in optimization. It describes 20 leading meta-heuristic and evolutionary algorithms and presents discussions and assessments of their performance in solving optimization problems from several fields of engineering. The book features clear and concise principles and presents detailed descriptions of leading methods such as the pattern search (PS) algorithm, the genetic algorithm (GA), the simulated annealing (SA) algorithm, the Tabu search (TS) algorithm, the ant colony optimization (ACO), and the particle swarm optimization (PSO) technique. Chapter 1 of Meta-heuristic and Evolutionary Algorithms for Engineering Optimization provides an overview of optimization and defines it by presenting examples of optimization problems in different engineering domains. Chapter 2 presents an introduction to meta-heuristic and evolutionary algorithms and links them to engineering problems. Chapters 3 to 22 are each devoted to a separate algorithm— and they each start with a brief literature review of the development of the algorithm, and its applications to engineering problems. The principles, steps, and execution of the algorithms are described in detail, and a pseudo code of the algorithm is presented, which serves as a guideline for coding the algorithm to solve specific applications. This book: Introduces state-of-the-art metaheuristic algorithms and their applications to engineering optimization; Fills a gap in the current literature by compiling and explaining the various meta-heuristic and evolutionary algorithms in a clear and systematic manner; Provides a step-by-step presentation of each algorithm and guidelines for practical implementation and coding of algorithms; Discusses and assesses the performance of metaheuristic algorithms in multiple problems from many fields of engineering; Relates optimization algorithms to engineering problems employing a unifying approach. Meta-heuristic and Evolutionary Algorithms for Engineering Optimization is a reference intended for students, engineers, researchers, and instructors in the fields of industrial engineering, operations research, optimization/mathematics, engineering optimization, and computer science. OMID BOZORG-HADDAD, PhD, is Professor in the Department of Irrigation and Reclamation Engineering at the University of Tehran, Iran. MOHAMMAD SOLGI, M.Sc., is Teacher Assistant for M.Sc. courses at the University of Tehran, Iran. HUGO A. LOÁICIGA, PhD, is Professor in the Department of Geography at the University of California, Santa Barbara, United States of America.
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| Release | : 1908 |
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The watershed management approach is a framework for addressing water quality problems at a watershed scale in an integrated manner that considers many conflicting issues including cost, environmental impact and equity in evaluating alternative control strategies. This framework enhances the capabilities of current environmental analysis frameworks by the inclusion of additional systems analytic tools such as optimization algorithms that enable efficient search for cost effective control strategies and uncertainty analysis procedures that estimate the reliability in achieving water quality targets. Traditional optimization procedures impose severe restrictions in using complex nonlinear environmental processes within a systematic search. Hence, genetic algorithms (GAs), a class of general, probabilistic, heuristic, global, search procedures, are used. Current implementation of this framework is coupled with US EPA's BASINS software system. A component of the current research is also the development of GA object classes and optimization model classes for generic use. A graphical user interface allows users to formulate mathematical programming problems and solve them using GA methodology. This set of GA object and the user interface classes together comprise the Generic Genetic Algorithm Based Optimizer (GeGAOpt), which is demonstrated through applications in solving interactively several unconstrained as well as constrained function optimization problems. Design of these systems is based on object oriented paradigm and current software engineering practices such as object oriented analysis (OOA) and object oriented design (OOD). The development follows the waterfall model for software development. The Unified Modeling Language (UML) is used for the design. The implementation is carried out using the JavaTM programming environment.
| Author | : |
| Publisher | : |
| Total Pages | : |
| Release | : 2001 |
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Watershed management requires consideration of a multitude of factors affecting water quality at the watershed-scale while integrating point and non-point sources of pollution and control. While the existing water quality modeling systems and associated quantitative tools can assist in some aspects of Total Maximum Daily Load (TMDL) development for a watershed, their abilities to assist in determining efficient management strategies are limited. Typically, the best a user can do is employ these tools manually to explore the solution space via a trial-and-error process, which is inefficient for finding management strategies that consider water quality as well as a multitude of other design issues simultaneously. Recent implementation of the STAR (STrategy, Analysis, and Reporting) system incorporates a set of systems analytic tools to assist decisions-makers explore and identify alternative management strategies. The main engine of the STAR system is a genetic algorithm-based optimization technique, which is coupled with additional tools such as an uncertainty propagation tool, a solution reporting system, and an incremental strategy development system to form a decision support framework. This paper describes some of the capabilities of this framework through several illustrative scenarios for the Yellow River watershed in Gwinnett County, Georgia, which conducted a comprehensive, countywide TMDL investigation to assess the current water quality conditions. The STAR system's capabilities are employed to identify ways to achieve minimum total phosphorous (TP) levels via point and nonpoint source controls, as well as characterize the implications of future urban development on TP levels. Noninferior tradeoffs between urban development and TP levels at different degrees of point source controls are generated. The range of uses of the STAR system in considering the integrated effect of point and non-point sources in watershed management is demonstrated throughout these.
| Author | : David G. Mayer |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 110 |
| Release | : 2012-12-06 |
| Genre | : Computers |
| ISBN | : 1461517176 |
Evolutionary Algorithms and Agricultural Systems deals with the practical application of evolutionary algorithms to the study and management of agricultural systems. The rationale of systems research methodology is introduced, and examples listed of real-world applications. It is the integration of these agricultural systems models with optimization techniques, primarily genetic algorithms, which forms the focus of this book. The advantages are outlined, with examples of agricultural models ranging from national and industry-wide studies down to the within-farm scale. The potential problems of this approach are also discussed, along with practical methods of resolving these problems. Agricultural applications using alternate optimization techniques (gradient and direct-search methods, simulated annealing and quenching, and the tabu search strategy) are also listed and discussed. The particular problems and methodologies of these algorithms, including advantageous features that may benefit a hybrid approach or be usefully incorporated into evolutionary algorithms, are outlined. From consideration of this and the published examples, it is concluded that evolutionary algorithms are the superior method for the practical optimization of models of agricultural and natural systems. General recommendations on robust options and parameter settings for evolutionary algorithms are given for use in future studies. Evolutionary Algorithms and Agricultural Systems will prove useful to practitioners and researchers applying these methods to the optimization of agricultural or natural systems, and would also be suited as a text for systems management, applied modeling, or operations research.
| Author | : Adriana Debora Piemonti |
| Publisher | : |
| Total Pages | : 197 |
| Release | : 2016 |
| Genre | : Genetic algorithms |
| ISBN | : |
Degradation of watersheds is a major concern in areas where adverse climate effects and unsustainable use of the natural resources have caused extensive stresses to watershed systems (e.g., increased floods, increased droughts, worsened in-stream water quality) through the years. While considerable efforts are being made to generate technical solutions that focus on plans of spatially-distributed conservation practices (e.g., Wetlands, Filter Strips, Grassed Waterways, Crop Management practices, etc.) for restoration of existing conditions in the watersheds, adoption and implementation of these solutions require a better understanding of constraints faced by affected stakeholders and decision makers. Participatory modeling and design approaches have, as a result, become popular in the recent past to support a community's engagement during the modeling process and during development of potential scenarios of plans (or, design alternatives). And now, with new and ongoing developments in Web 2.0 technologies, there is an even greater need for research that examines how large number of stakeholders can be engaged in the development of design alternatives via the internet-based, decision support environments. The overarching goal of this research is to investigate how stakeholder participation ("humans") and Interactive Genetic Algorithms ("computer") can be coupled in a web-based watershed decision support system (DSS) called WRESTORE (Watershed REstoration using Spatio Temporal Optimization of REsources- http://wrestore.iupui.edu/), in order to generate user-preferred design alternatives of distributed conservation practices on a watershed landscape. An important component of this goal is to also improve the understanding of how human behavior on the graphical user interface (GUI) of the DSS can be observed and evaluated in real-time, and then learned from to further improve the performance of the underlying search algorithm. Four specific objectives were addressed in this work to accomplish the overall goal: · Objective 1: Observe interactions of multiple users with the GUI of a web-based watershed DSS (WRESTORE, http://wrestore.iupui.edu/) during interactive search experiments, and then use Usability metrics (response times, clicking events and confidence levels) to evaluate the differences and similarities in user behaviors and interactions. · Objective 2: Examine relationships between the type of users (e.g., stakeholders versus surrogates), the Usability metrics, and patterns in the watershed-scale plans of conservation practices generated by the multi-objective Interactive Genetic Algorithm embedded in WRESTORE. · Objective 3: Examine relationships between the type of users, the Usability metrics, and patterns in the user-preferred, sub-basin-scale plans of conservation practices generated by the multi-objective Interactive Genetic Algorithm embedded in WRESTORE. · Objective 4: Develop and test novel human-guided search operators that adaptively learn for patterns in user-preferred alternatives generated by the multi-objective Interactive Genetic Algorithm, and, as a result, improve the convergence rate of the search algorithm for generating design alternatives that conserve these learned patterns. Results show that there is a clear difference on how different types of users interact with the Interactive Optimization system. The observed relationship between confidence levels, time spent on a task, and number of mouse clicking events, indicated that participants who were able to use the WRESTORE GUI to gather more information and had a higher rate of time per number of clicks, tended to increase their levels of self-confidence in their own feedback. Also, when engaging with watershed stakeholders versus non-stakeholders (or, surrogates), 67% of the stakeholder participants steadily increased their average self-confidence levels as they continued to interact with the tool, in contrast to only 29% of surrogate participants who also showed an increase in their self-confidence levels through time. Such usability and confidence level evaluations provide assessments on which participant was potentially generating reliable feedback data for the search algorithm to use. An analysis of design alternatives generated by the individuals in both stakeholder and non-stakeholder groups showed that a majority (67%) of the stakeholder participants found a higher percentage (on and average 52%) of preferred design alternatives via the interactive search process. Also, users who were focused on assessing the suitability of design alternatives for the entire watershed trended to demonstrate a bias for one of the watershed-scale objective functions. In contrast, users, who were focused on assessing the suitability of design alternatives at only a few local sub-basins in the watershed, did not demonstrate any clear bias for any one of the watershed-scale objective functions. Additionally, patterns were observed in the design of decision alternatives generated by the human-centered search process, which further divulged potential user preferences related to the decision space for example, whether a specific participant preferred a certain practice over another, or a certain location over another for a specific practice. Finally, to improve the convergence rates of the Interactive Genetic Algorithm in WRESTORE, we investigated whether observed patterns in decisions (especially, when users were focused on local sub-regions of the watershed) can be used to improve the search for user-desire designs. A novel Interactive Genetic Algorithm with adaptive, human-guided, selection, crossover and mutation operators was proposed. The new algorithm was tested with six types of simulated participants (three deterministic and three probabilistic users) developed from the feedback data of three real participants. Results of search experiments with the novel adaptive IGA operators indicated a faster convergence than the default IGA, for two out of three deterministic simulated users. However, none of the probabilistic user showed a convergence different than the default values. This indicates that while current results indicate promise, there is need for additional research on adaptive, human-guided IGA operators, especially when noisy/stochastic users participate in the search. Additionally, adaptation of search operators have the potential to improve convergence rates when participatory design is done via Interactive Genetic Algorithms.
