Hossein Bonakdari – författare

Water Engineering Modeling and Mathematic Tools provides an informative resource for practitioners who want to learn more about different techniques and models in water engineering and their practical applications and case studies. The book provides modelling theories in an easy-to-read format verified with on-site models for specific regions and scenarios. Users will find this to be a significant contribution to the development of mathematical tools, experimental techniques, and data-driven models that support modern-day water engineering applications. Civil engineers, industrialists, and water management experts should be familiar with advanced techniques that can be used to improve existing systems in water engineering.

This book provides key ideas on recently developed machine learning methods and AI modelling. It will serve as a common platform for practitioners who need to become familiar with the latest developments of computational techniques in water engineering.

Includes firsthand experience about artificial intelligence models, utilizing case studies Describes biological, physical and chemical techniques for the treatment of surface water, groundwater, sea water and rain/snow Presents the application of new instruments in water engineering

Stochastic Modeling: A Thorough Guide to Evaluate, Pre-Process, Model and Compare Time Series with MATLAB Software allows for new avenues in time series analysis and predictive modeling which summarize more than ten years of experience in the application of stochastic models in environmental problems. The book introduces a variety of different topics in time series in the modeling and prediction of complex environmental systems. Most importantly, all codes are user-friendly and readers will be able to use them for their cases. Users who may not be familiar with MATLAB software can also refer to the appendix.

This book also guides the reader step-by-step to learn developed codes for time series modeling, provides required toolboxes, explains concepts, and applies different tools for different types of environmental time series problems.

Provides video tutorials on the use of codes Includes a companion site with 3,000 lines of programming, 70 principal codes and 100 pseudo codes Highlights multiple methods to Illustrate each problem

Intelligence Systems for Earth, Environmental and Planetary Sciences: Methods, Models and Applications provides cutting-edge theory and applications of modern-day artificial intelligence and data science in the Earth, environment, and planetary science fields. The book is divided into three sections: (i) Methods, covering the fundamentals of intelligence systems, along with an introduction to the preparation of datasets; (ii) Models, detailing model development, data assimilation, and techniques in each field; and (iii) Applications, presenting case studies of artificial intelligence and data science solutions to Earth, environmental, and planetary sciences problems, as well as future perspectives.Intelligence Systems for Earth, Environmental and Planetary Sciences will be of interest to students, academics, and postgraduate professionals in the field of applied sciences, Earth, environmental, and planetary sciences and would also serve as an excellent companion resource to courses studying artificial intelligence applications for theoretical and practical studies in Earth, environmental, and planetary sciences.

Facilitates the application of artificial intelligence and data science systems to create comprehensive methodologies for analyzing, processing, predicting, and management strategies in the fields of Earth, environment, and planetary scienceDeveloped with an interdisciplinary framework, with an aim to promote artificial intelligence models for real-time Earth systemsIncludes a section on case studies of artificial intelligence and data science solutions to Earth, environmental, and planetary sciences problems, as well as future perspectives

Machine Learning in Earth, Environmental and Planetary Sciences: Theoretical and Practical Applications is a practical guide on implementing different variety of extreme learning machine algorithms to Earth and environmental data. The book provides guided examples using real-world data for numerous novel and mathematically detailed machine learning techniques that can be applied in Earth, environmental, and planetary sciences, including detailed MATLAB coding coupled with line-by-line descriptions of the advantages and limitations of each method. The book also presents common postprocessing techniques required for correct data interpretation.

This book provides students, academics, and researchers with detailed understanding of how machine learning algorithms can be applied to solve real case problems, how to prepare data, and how to interpret the results.

Describes how to develop different schemes of machine learning techniques and apply to Earth, environmental and planetary data Provides detailed, guided line-by-line examples using real-world data, including the appropriate MATLAB codes Includes numerous figures, illustrations and tables to help readers better understand the concepts covered

Decision-making is a key factor to achieve success in any discipline, especially in a field like civil engineering, which is based on calculations and requires large amounts of information being taken into account. Most processes and procedures are a compendium of many different tasks and requirements specific to each project under development, and making decisions in such environments can often be an arduous endeavor. That is why the need for analytical criteria capable of assisting with untangling complex scenarios has arisen preponderantly.As an all-encompassing resource, Multicriteria Decision-Making Analysis for Civil Engineering Applications facilitates civil engineers by outlining state-of-the-art techniques for quantitative decision-making to optimally select the appropriate approach when faced with operational issues or to prioritize among multiple options.Authored by recognized experts in the field, this book proves to be a balanced reference volume that is essential not just for civil engineers, but also for a wide variety of audiences in interconnected disciplines.

Presents a systematic framework of methodological solutions helping readers to make decisions quickly and accuratelyFeatures several real-life case studies that support understanding and provide reliable actionable guidanceIncludes the theoretical underpinnings of decision support tools and emphasizes multicriteria decision analysis techniques applied to civil engineering projectsOffers civil engineers a structured approach to tackle complex decisions and establish priorities in their projectsIs accompanied by an online companion site that includes Excel worksheets, demonstrating step-by-step processes, numerical simulations, and worked-out examples

Computational Methods for Time-Series Analyses in Earth Sciences bridges the gap between theoretical knowledge and practical application, offering a deep dive into the utilization of R programming for managing, analyzing, and forecasting time-series data within the realm of Earth sciences. It systematically unfolds the layers of data manipulation, graphical representation, and sampling to prepare the reader for complex analyses and predictive modeling from the basics of signal processing to the nuances of machine learning. It presents cutting-edge techniques, such as neural networks, kernel-based methods, and evolutionary algorithms, specifically tailored to tackle challenges, and provides practical case studies to aid readers with utilizing the techniques covered.Computational Methods for Time-Series Analyses in Earth Sciences is a valuable resource for scientists, researchers, and students delving into the intricacies of Earth's environmental patterns and cycles through the lens of computational analysis and guides readers through various computational approaches to deciphering spatial and temporal data.Focuses on the use of R for time-series analysis and the application of these methods directly to Earth and environmental datasetsIntegrates Machine Learning techniques, enabling readers to explore advanced computational methods for forecasting and modelingIncludes case studies with real-world applications, providing readers with examples on how to translate computational skills into tangible outcomes

This book offers a modern perspective on hazard and risk assessment in civil engineering, with special attention to sustainable infrastructure development. Through its comprehensive coverage of theoretical concepts and its thoughtful integration of sustainability into engineering practice, it serves as a valuable resource for professionals, researchers, and students.It bridges the gap between hazard and risk assessment and the principles of sustainable development, presenting a holistic approach to identifying, assessing, and mitigating risks associated with natural hazards while advancing sustainable infrastructure solutions. Beyond outlining core concepts, the book provides practical examples, user‑friendly code, and step‑by‑step software guidance. Readers will find numerous illustrations, diagrams, tables, and case studies that deepen understanding and support the application of risk assessment methodologies.Ultimately, the book is a guide to unlocking innovative solutions and shaping the future of civil engineering. Civil engineers, environmental scientists, policymakers, and anyone interested in understanding risk assessment will benefit from its insights into building a sustainable, resilient world.