Metaheuristic Algorithms and Neural Networks in Hydrology

Metaheuristic Algorithms and Neural Networks in Hydrology
Author: Kuok King Kuok
Publisher: Cambridge Scholars Publishing
Total Pages: 231
Release: 2024-08-28
Genre: Science
ISBN: 1036408051

This book summarizes the latest research and developments related to the application of nature-inspired metaheuristic algorithms coupled with artificial neural networks (ANNs) in hydrology. The book covers the theoretical foundations, models and methods, structure, frameworks and analysis of applying novel ANNs in hydrology. It starts with the introduction of ANNs as a black box model, followed by the coupling of various metaheuristic algorithms with ANNs to form novel neural network models for solving real-world problems in hydrology, including Particle Swarm Optimization (PSO) for rainfall-runoff modeling, Bat Optimization (Bat) and Cuckoo Search Optimization (CSO) for future rainfall prediction, the Whale Optimization Algorithm (WOA) and Salp Swarm Optimization (SSO) for future water level prediction, Grey Wolf Optimization (GWO), Multi-Verse Optimization (MVO), the Sine Cosine Algorithm (SCA) and the Hybrid Sine Cosine and Fitness Dependent Optimizer (SC-FDO) for imputing missing rainfall data.

Nature-Inspired Methods for Metaheuristics Optimization

Nature-Inspired Methods for Metaheuristics Optimization
Author: Fouad Bennis
Publisher: Springer Nature
Total Pages: 503
Release: 2020-01-17
Genre: Business & Economics
ISBN: 3030264580

This book gathers together a set of chapters covering recent development in optimization methods that are inspired by nature. The first group of chapters describes in detail different meta-heuristic algorithms, and shows their applicability using some test or real-world problems. The second part of the book is especially focused on advanced applications and case studies. They span different engineering fields, including mechanical, electrical and civil engineering, and earth/environmental science, and covers topics such as robotics, water management, process optimization, among others. The book covers both basic concepts and advanced issues, offering a timely introduction to nature-inspired optimization method for newcomers and students, and a source of inspiration as well as important practical insights to engineers and researchers.

Advanced Metaheuristic Methods in Big Data Retrieval and Analytics

Advanced Metaheuristic Methods in Big Data Retrieval and Analytics
Author: Bouarara, Hadj Ahmed
Publisher: IGI Global
Total Pages: 340
Release: 2018-11-02
Genre: Computers
ISBN: 1522573399

The amount of data shared and stored on the web and other document repositories is steadily on the rise. Unfortunately, this growth increases inefficiencies and difficulties when trying to find the most relevant and up-to-date information due to unstructured data. Advanced Metaheuristic Methods in Big Data Retrieval and Analytics examines metaheuristic techniques as an important alternative model for solving complex problems that are not treatable by deterministic methods. Recent studies suggest that IR and biomimicry can be used together for several application problems in big data and internet of things, especially when conventional methods would be too expensive or difficult to implement. Featuring coverage on a broad range of topics such as ontology, plagiarism detection, and machine learning, this book is ideally designed for engineers, graduate students, IT professionals, and academicians seeking an overview of new trends in information retrieval in big data.

Advancing Technology Industrialization Through Intelligent Software Methodologies, Tools and Techniques

Advancing Technology Industrialization Through Intelligent Software Methodologies, Tools and Techniques
Author: H. Fujita
Publisher: IOS Press
Total Pages: 770
Release: 2019-09-17
Genre: Computers
ISBN: 1643680137

Software has become ever more crucial as an enabler, from daily routines to important national decisions. But from time to time, as society adapts to frequent and rapid changes in technology, software development fails to come up to expectations due to issues with efficiency, reliability and security, and with the robustness of methodologies, tools and techniques not keeping pace with the rapidly evolving market. This book presents the proceedings of SoMeT_19, the 18th International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, held in Kuching, Malaysia, from 23–25 September 2019. The book explores new trends and theories that highlight the direction and development of software methodologies, tools and techniques, and aims to capture the essence of a new state of the art in software science and its supporting technology, and to identify the challenges that such a technology will have to master. The book also investigates other comparable theories and practices in software science, including emerging technologies, from their computational foundations in terms of models, methodologies, and tools. The 56 papers included here are divided into 5 chapters: Intelligent software systems design and techniques in software engineering; Machine learning techniques for software systems; Requirements engineering, software design and development techniques; Software methodologies, tools and techniques for industry; and Knowledge science and intelligent computing. This comprehensive overview of information systems and research projects will be invaluable to all those whose work involves the assessment and solution of real-world software problems.

Metaheuristic Applications in Structures and Infrastructures

Metaheuristic Applications in Structures and Infrastructures
Author: Xin-She Yang
Publisher: Newnes
Total Pages: 577
Release: 2013-01-31
Genre: Technology & Engineering
ISBN: 0123983797

Due to an ever-decreasing supply in raw materials and stringent constraints on conventional energy sources, demand for lightweight, efficient and low-cost structures has become crucially important in modern engineering design. This requires engineers to search for optimal and robust design options to address design problems that are commonly large in scale and highly nonlinear, making finding solutions challenging. In the past two decades, metaheuristic algorithms have shown promising power, efficiency and versatility in solving these difficult optimization problems. This book examines the latest developments of metaheuristics and their applications in structural engineering, construction engineering and earthquake engineering, offering practical case studies as examples to demonstrate real-world applications. Topics cover a range of areas within engineering, including big bang-big crunch approach, genetic algorithms, genetic programming, harmony search, swarm intelligence and some other metaheuristic methods. Case studies include structural identification, vibration analysis and control, topology optimization, transport infrastructure design, design of reinforced concrete, performance-based design of structures and smart pavement management. With its wide range of everyday problems and solutions, Metaheursitic Applications in Structures and Infrastructures can serve as a supplementary text for design courses and computation in engineering as well as a reference for researchers and engineers in metaheuristics, optimization in civil engineering and computational intelligence. - Review of the latest development of metaheuristics in engineering. - Detailed algorithm descriptions with focus on practical implementation. - Uses practical case studies as examples and applications.

Handbook of HydroInformatics

Handbook of HydroInformatics
Author: Saeid Eslamian
Publisher: Elsevier
Total Pages: 422
Release: 2022-12-06
Genre: Science
ISBN: 0128219521

Handbook of HydroInformatics Volume III: Water Data Management Best Practices presents the latest and most updated data processing techniques that are fundamental to Water Science and Engineering disciplines. These include a wide range of the new methods that are used in hydro-modeling such as Atmospheric Teleconnection Pattern, CONUS-Scale Hydrologic Modeling, Copula Function, Decision Support System, Downscaling Methods, Dynamic System Modeling, Economic Impacts and Models, Geostatistics and Geospatial Frameworks, Hydrologic Similarity Indices, Hydropower/Renewable Energy Models, Sediment Transport Dynamics Advanced Models, Social Data Mining, and Wavelet Transforms. This volume is an example of true interdisciplinary work. The audience includes postgraduates and above interested in Water Science, Geotechnical Engineering, Soil Science, Civil Engineering, Chemical Engineering, Computer Engineering, Engineering, Applied Science, Earth and Geoscience, Atmospheric Science, Geography, Environment Science, Natural Resources, Mathematical Science, and Social Sciences. It is a fully comprehensive handbook which provides all the information needed related to the best practices for managing water data. - Contributions from global experts in the fields of data management research, climate change and resilience, insufficient data problem, etc. - Thorough applied examples and case studies in each chapter, providing the reader with real world scenarios for comparison. - Includes a wide range of new methods that are used in hydro-modeling, with step-by-step guides on how to use them.

Metaheuristics for Machine Learning

Metaheuristics for Machine Learning
Author: Mansour Eddaly
Publisher: Springer Nature
Total Pages: 231
Release: 2023-03-13
Genre: Computers
ISBN: 9811938881

Using metaheuristics to enhance machine learning techniques has become trendy and has achieved major successes in both supervised (classification and regression) and unsupervised (clustering and rule mining) problems. Furthermore, automatically generating programs via metaheuristics, as a form of evolutionary computation and swarm intelligence, has now gained widespread popularity. This book investigates different ways of integrating metaheuristics into machine learning techniques, from both theoretical and practical standpoints. It explores how metaheuristics can be adapted in order to enhance machine learning tools and presents an overview of the main metaheuristic programming methods. Moreover, real-world applications are provided for illustration, e.g., in clustering, big data, machine health monitoring, underwater sonar targets, and banking.