دانلود کتاب Irregularities and prediction of major disasters

Although scientists have effectively employed the concepts of probability to address the complex problem of prediction, modern science still falls short in establishing true predictions with meaningful lead times of zero-probability major disasters. The recent earthquakes in Haiti, Chile, and China are tragic reminders of the critical need for improved methods of predicting natural disasters. Drawing on their vast practical experience and theoretical studies, Dr. Yi Lin and Professor Shoucheng OuYang examine some of the problems that exist in the modern system of science to provide the understanding required to improve our ability to forecast and prepare for such events. Presenting a series of new understandings, theories, and a new system of methodology, Irregularities and Prediction of Major Disasters simplifies the world-class problem of prediction into a series of tasks that can be learned, mastered, and applied in the analysis and prediction of forthcoming changes in materials or fluids. These internationally respected authors introduce their novel method of digitization for dealing with irregular information, proven effective for predicting transitional changes in events. They also: Unveil a new methodology for forecasting zero-probability natural disasters Highlight the reasons for common forecasting failures Propose a method for resolving the mystery of nonlinearity Include numerous real-life case studies that illustrate how to properly digitize available information Supply proven methods for forecasting small-probability natural disasters This authoritative resource provides a systematic discussion of the non-evolutionality of the modern system of science—analyzing its capabilities and limitations. By touching on the need for change in some of the fundamentals in basic scientific theories and relevant methodologies, this book provides the scientific community with the understanding and methodology required to forecast zero-probability major disasters with greatly improved accuracy.