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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.
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.
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.
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