Bøger af Omid Bozorg-Haddad

This book provides a comprehensive approach to all aspects of water-related subjects affected by climate change that expand readers' attitudes toward future of the management strategies and improve management plans. It summarizes climate change scenarios, models, downscaling methods, and how to select the appropriate method. It also introduces practical steps in assessing climate change impacts on water issues through introducing hydrological models and climate change data applications in hydrologic analysis. The book caters to specialist readers who are interested in analyzing climate change effects on water resources, and related issues can gain a profound understanding of the practical concepts and step-by-step analysis, which is enriched with real case studies all around the world. Moreover, readers will be familiar with potential mitigation and adaptation measures in sustainable water engineering, considering the results of hydrologic modeling.

This book provides a comprehensive yet fresh perspective for the cutting-edge CI-oriented approaches in water resources planning and management. The book takes a deep dive into topics like meta-heuristic evolutionary optimization algorithms (e.g., GA, PSA, etc.), data mining techniques (e.g., SVM, ANN, etc.), probabilistic and Bayesian-oriented frameworks, fuzzy logic, AI, deep learning, and expert systems. These approaches provide a practical approach to understand and resolve complicated and intertwined real-world problems that often imposed serious challenges to traditional deterministic precise frameworks. The topic caters to postgraduate students and senior researchers who are interested in computational intelligence approach to issues stemming from water and environmental sciences.

Clear and effective instruction on MADM methods for students, researchers, and practitioners.A Handbook on Multi-Attribute Decision-Making Methods describes multi-attribute decision-making (MADM) methods and provides step-by-step guidelines for applying them. The authors describe the most important MADM methods and provide an assessment of their performance in solving problems across disciplines. After offering an overview of decision-making and its fundamental concepts, this book covers 20 leading MADM methods and contains an appendix on weight assignment methods. Chapters are arranged with optimal learning in mind, so you can easily engage with the content found in each chapter. Dedicated readers may go through the entire book to gain a deep understanding of MADM methods and their theoretical foundation, and others may choose to review only specific chapters. Each standalone chapter contains a brief description of prerequisite materials, methods, and mathematical concepts needed to cover its content, so you will not face any difficulty understanding single chapters. Each chapter:* Describes, step-by-step, a specific MADM method, or in some cases a family of methods* Contains a thorough literature review for each MADM method, supported with numerous examples of the method's implementation in various fields* Provides a detailed yet concise description of each method's theoretical foundation* Maps each method's philosophical basis to its corresponding mathematical framework* Demonstrates how to implement each MADM method to real-world problems in a variety of disciplinesIn MADM methods, stakeholders' objectives are expressible through a set of often conflicting criteria, making this family of decision-making approaches relevant to a wide range of situations. A Handbook on Multi-Attribute Decision-Making Methods compiles and explains the most important methodologies in a clear and systematic manner, perfect for students and professionals whose work involves operations research and decision making.

A detailed review of a wide range of meta-heuristic and evolutionary algorithms in a systematic manner and how they relate to engineering optimization problemsThis 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.