Heavy-Tailed Distributions in Disaster Analysis [electronic resource] /
Pisarenko, V.author.
Rodkin, M.author.
Mathematically, natural disasters of all types are characterized by heavy tailed distributions. The analysis of such distributions with common methods, such as averages and dispersions, can therefore lead to erroneous conclusions. The statistical methods described in this book avoid such pitfalls. Seismic disasters are studied, primarily thanks to the availability of an ample statistical database. New approaches are presented to seismic risk estimation and forecasting the damage caused by earthquakes, ranging from typical, moderate events to very rare, extreme disasters. Analysis of these latter events is based on the limit theorems of probability and the duality of the generalized Pareto distribution and generalized extreme value distribution. It is shown that the parameter most widely used to estimate seismic risk – Mmax, the maximum possible earthquake value – is potentially non-robust. Robust analogues of this parameter are suggested and calculated for some seismic catalogues. Trends in the costs inferred by damage from natural disasters as related to changing social and economic situations are examined for different regions. The results obtained argue for sustainable development, whereas entirely different, incorrect conclusions can be drawn if the specific properties of the heavy-tailed distribution and change in completeness of data on natural hazards are neglected. Audience: This pioneering work is directed at risk assessment specialists in general, seismologists, administrators and all those interested in natural disasters and their impact on society.
Description based upon print version of record.
Includes bibliographical references (p. 181-188) and index.
Distributions of Characteristics of Natural Disasters: Data and Classification -- Models for the Generation of Distributions of Different Types -- Nonparametric Methods in the Study of Distributions -- Nonlinear and Linear Growth of Cumulative Effects of Natural Disasters -- The Nonlinear and Linear Modes of Growth of the Cumulative Seismic Moment -- Estimating the Uppermost Tail of a Distribution -- Relationship Between Earthquake Losses and Social and Economic Situation.
Mathematically, natural disasters of all types are characterized by heavy tailed distributions. The analysis of such distributions with common methods, such as averages and dispersions, can therefore lead to erroneous conclusions. The statistical methods described in this book avoid such pitfalls. Seismic disasters are studied, primarily thanks to the availability of an ample statistical database. New approaches are presented to seismic risk estimation and forecasting the damage caused by earthquakes, ranging from typical, moderate events to very rare, extreme disasters. Analysis of these latter events is based on the limit theorems of probability and the duality of the generalized Pareto distribution and generalized extreme value distribution. It is shown that the parameter most widely used to estimate seismic risk – Mmax, the maximum possible earthquake value – is potentially non-robust. Robust analogues of this parameter are suggested and calculated for some seismic catalogues. Trends in the costs inferred by damage from natural disasters as related to changing social and economic situations are examined for different regions. The results obtained argue for sustainable development, whereas entirely different, incorrect conclusions can be drawn if the specific properties of the heavy-tailed distribution and change in completeness of data on natural hazards are neglected. Audience: This pioneering work is directed at risk assessment specialists in general, seismologists, administrators and all those interested in natural disasters and their impact on society.
Geophysics.
Natural disasters.
Applied mathematics.
Engineering mathematics.
Statistics .
Earth sciences.
Geophysics and Environmental Physics.
Natural Hazards.
Mathematical and Computational Engineering.
Geophysics/Geodesy.
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
Earth Sciences, general.