Time Series Modelling In Earth Sciences
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| Author | : Jean-Philippe Montillet |
| Publisher | : Springer |
| Total Pages | : 438 |
| Release | : 2019-08-16 |
| Genre | : Science |
| ISBN | : 3030217183 |
This book provides an essential appraisal of the recent advances in technologies, mathematical models and computational software used by those working with geodetic data. It explains the latest methods in processing and analyzing geodetic time series data from various space missions (i.e. GNSS, GRACE) and other technologies (i.e. tide gauges), using the most recent mathematical models. The book provides practical examples of how to apply these models to estimate seal level rise as well as rapid and evolving land motion changes due to gravity (ice sheet loss) and earthquakes respectively. It also provides a necessary overview of geodetic software and where to obtain them.
| Author | : B.K. Sahu |
| Publisher | : CRC Press |
| Total Pages | : 304 |
| Release | : 2021-07-01 |
| Genre | : Technology & Engineering |
| ISBN | : 1000445828 |
Including the latest theories and applications of time series modelling, this book is intended for students, faculties and professionals with a background in multivariate statistics. Highlighting linear methods to yield ARIMA, SARIMA models and their multivariate (vector) extensions, the text also draws attention to non-linear methods, as well as state-space, dynamic linear, wavelet, volatility and long memory models. Also included are several solved case studies and exercises from the fields of mining, ore genesis, earthquakes, and climatology.
| Author | : Victor Privalsky |
| Publisher | : Springer Nature |
| Total Pages | : 253 |
| Release | : 2020-11-22 |
| Genre | : Science |
| ISBN | : 3030580555 |
This book gives the reader the basic knowledge of the theory of random processes necessary for applying to study climatic time series. It contains many examples in different areas of time series analysis such as autoregressive modelling and spectral analysis, linear extrapolation, simulation, causality, relations between scalar components of multivariate time series, and reconstructions of climate data. As an important feature, the book contains many practical examples and recommendations about how to deal and how not to deal with applied problems of time series analysis in climatology or any other science where the time series are short.
| Author | : Zhihua Zhang |
| Publisher | : Springer |
| Total Pages | : 293 |
| Release | : 2017-11-09 |
| Genre | : Science |
| ISBN | : 3319673408 |
This book offers comprehensive information on the theory, models and algorithms involved in state-of-the-art multivariate time series analysis and highlights several of the latest research advances in climate and environmental science. The main topics addressed include Multivariate Time-Frequency Analysis, Artificial Neural Networks, Stochastic Modeling and Optimization, Spectral Analysis, Global Climate Change, Regional Climate Change, Ecosystem and Carbon Cycle, Paleoclimate, and Strategies for Climate Change Mitigation. The self-contained guide will be of great value to researchers and advanced students from a wide range of disciplines: those from Meteorology, Climatology, Oceanography, the Earth Sciences and Environmental Science will be introduced to various advanced tools for analyzing multivariate data, greatly facilitating their research, while those from Applied Mathematics, Statistics, Physics, and the Computer Sciences will learn how to use these multivariate time series analysis tools to approach climate and environmental topics.
| Author | : Manfred Mudelsee |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 497 |
| Release | : 2010-08-26 |
| Genre | : Science |
| ISBN | : 9048194822 |
Climate is a paradigm of a complex system. Analysing climate data is an exciting challenge, which is increased by non-normal distributional shape, serial dependence, uneven spacing and timescale uncertainties. This book presents bootstrap resampling as a computing-intensive method able to meet the challenge. It shows the bootstrap to perform reliably in the most important statistical estimation techniques: regression, spectral analysis, extreme values and correlation. This book is written for climatologists and applied statisticians. It explains step by step the bootstrap algorithms (including novel adaptions) and methods for confidence interval construction. It tests the accuracy of the algorithms by means of Monte Carlo experiments. It analyses a large array of climate time series, giving a detailed account on the data and the associated climatological questions. This makes the book self-contained for graduate students and researchers.
| Author | : Martin H. Trauth |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 294 |
| Release | : 2007 |
| Genre | : Computers |
| ISBN | : 3540727485 |
Introduces methods of data analysis in geosciences using MATLAB such as basic statistics for univariate, bivariate and multivariate datasets, jackknife and bootstrap resampling schemes, processing of digital elevation models, gridding and contouring, geostatistics and kriging, processing and georeferencing of satellite images, digitizing from the screen, linear and nonlinear time-series analysis and the application of linear time-invariant and adaptive filters. Includes a brief description of each method and numerous examples demonstrating how MATLAB can be used on data sets from earth sciences.
| Author | : Jose D. Salas |
| Publisher | : Water Resources Publication |
| Total Pages | : 502 |
| Release | : 1980 |
| Genre | : Science |
| ISBN | : 9780918334374 |
| Author | : Chunyan Li |
| Publisher | : Cambridge University Press |
| Total Pages | : 483 |
| Release | : 2022-05-05 |
| Genre | : Computers |
| ISBN | : 1108474276 |
Textbook for students and researchers in oceanography and Earth science on theory and practice of time series analysis using MATLAB.
| Author | : Hamid Reza Pourghasemi |
| Publisher | : Elsevier |
| Total Pages | : 800 |
| Release | : 2019-01-18 |
| Genre | : Science |
| ISBN | : 0128156953 |
Spatial Modeling in GIS and R for Earth and Environmental Sciences offers an integrated approach to spatial modelling using both GIS and R. Given the importance of Geographical Information Systems and geostatistics across a variety of applications in Earth and Environmental Science, a clear link between GIS and open source software is essential for the study of spatial objects or phenomena that occur in the real world and facilitate problem-solving. Organized into clear sections on applications and using case studies, the book helps researchers to more quickly understand GIS data and formulate more complex conclusions. The book is the first reference to provide methods and applications for combining the use of R and GIS in modeling spatial processes. It is an essential tool for students and researchers in earth and environmental science, especially those looking to better utilize GIS and spatial modeling. - Offers a clear, interdisciplinary guide to serve researchers in a variety of fields, including hazards, land surveying, remote sensing, cartography, geophysics, geology, natural resources, environment and geography - Provides an overview, methods and case studies for each application - Expresses concepts and methods at an appropriate level for both students and new users to learn by example
| Author | : Maurizio Petrelli |
| Publisher | : Springer Nature |
| Total Pages | : 229 |
| Release | : 2021-09-16 |
| Genre | : Science |
| ISBN | : 3030780554 |
This textbook introduces the use of Python programming for exploring and modelling data in the field of Earth Sciences. It drives the reader from his very first steps with Python, like setting up the environment and starting writing the first lines of codes, to proficient use in visualizing, analyzing, and modelling data in the field of Earth Science. Each chapter contains explicative examples of code, and each script is commented in detail. The book is minded for very beginners in Python programming, and it can be used in teaching courses at master or PhD levels. Also, Early careers and experienced researchers who would like to start learning Python programming for the solution of geological problems will benefit the reading of the book.
