Extreme Value Theory with Applications to Natural Hazards

This richly illustrated book describes statistical extreme value theory for the quantification of natural hazards, such as strong winds, floods and rainfall, and discusses an interdisciplinary approach to allow the theoretical methods to be applied. The approach consists of a number of steps: data selection and correction, non-stationary theory (to account for trends due to climate change), and selecting appropriate estimation techniques based on both decision-theoretic features (e.g., Bayesian theory), empirical robustness and a valid treatment of uncertainties. It also examines and critically reviews alternative approaches based on stochastic and dynamic numerical models, as well as recently emerging data analysis issues and presents large-scale, multidisciplinary, state-of-the-art case studies. 

Intended for all those with a basic knowledge of statistical methods interested in the quantification of natural hazards, the book is also a valuable resource for engineers conducting risk analyses in collaboration with scientists from other fields (such as hydrologists, meteorologists, climatologists). 

• Extreme value statistics
• Natural hazards
• Risk analysis
• Cumulated hazards
• Downscaling
• Stochastic and numerical modelling
• Non-stationarity and climate change
• Probabilistic modelling
• Historical data
• Nonstationarity
• Extreme Floods
• Extreme Temperature
• Meteorology
• Maritime Storm

• EDF Lab, Saclay-Chatou, and Sorbonne Université, Paris, France

  Nicolas Bousquet

• EDF Research and Development, London, UK

  Pietro Bernardara

Dr. Nicolas Bousquet is a mathematician specializing in probability and statistics. Trained in computer science, he received his Ph.D in Mathematics from the Paris XI University in 2006. He has developed Bayesian modeling methodologies to merge heterogeneous sources of information into decision support problems in uncertain environments, methods for sensitivity analysis and Monte Carlo acceleration methods within complex numerical models. Awarded Best Young European Statistician by ENBIS in 2016, he worked in industrial risk and environmental resource management at EDF R&D for 9 years and in collaboration with many public and international research centers. He was also an associate researcher at the Institut de Mathématique de Toulouse. Between 2017 and 2020, he was in charge of R&D at Quantmetry, a consulting firm specializing in Artificial Intelligence (AI), while also serving as an Associate Professor at Sorbonne University. He has published about 40 research articles and book chapters and in 2018 he directed the production of the first scientific book translated using artificial intelligence tools (Deep Learning, by Goodfellow, Bengio and Courville). Still an Associate Professor, he is currently the Deputy Head of the industrial AI joint laboratory SINCLAIR (EDF-Total-Thales) and a Expert Researcher at EDF R&D. 

Dr. Pietro Bernardara is a hydrologist and holds a Ph.D from the Politechnico di Milano (2004). With a strong background in applied statistics, he has developed numerous techniques for quantifying extreme natural hazards in river and marine environments to mitigate industrial risks. After working as an expert researcher at EDF R&D, then as a Natural Hazard R&D Manager at EDF Energy (UK), he currently heads the CEREA (Centre for Teaching and Research in Atmospheric Environment) at the Ecole des Ponts ParisTech, as well as the "Atmospheric Environment" Group at EDF R&D. He is the author or co-author of about thirty publications.

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