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© 2016

Studies on Time Series Applications in Environmental Sciences

Benefits

  • Interdisciplinary approach to time series modeling and forecasting applied to environmental sciences and computational geosciences

  • Presents Time Series Applications in Environmental Sciences, especially in the field of meteorology and hydrology

  • Provides various examples and alternative solutions

Book

Part of the Intelligent Systems Reference Library book series (ISRL, volume 103)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Alina Bărbulescu
    Pages 79-120
  3. Alina Bărbulescu
    Pages 145-151
  4. Alina Bărbulescu
    Pages 153-158
  5. Alina Bărbulescu
    Pages 159-187

About this book

Introduction

Time series analysis and modelling represent a large study field, implying the approach from the perspective of the time and frequency, with applications in different domains. Modelling hydro-meteorological time series is difficult due to the characteristics of these series, as long range dependence, spatial dependence, the correlation with other series. Continuous spatial data plays an important role in planning, risk assessment and decision making in environmental management.  

In this context, in this book we present various statistical tests and modelling techniques used for time series analysis, as well as applications to hydro-meteorological series from Dobrogea, a region situated in the south-eastern part of Romania, less studied till now. Part of the results are accompanied by their R code.  

Keywords

Artificial Neural Networks Atmospheric Forecasting Box-Jenkins Methodology Computational Geosciences Geophysical Modeling Geostatistics Intelligent Systems Support Vector Regression

Authors and affiliations

  1. 1.Ovidius University of ConstantaConstantaRomania

Bibliographic information

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Reviews

“I enjoyed reading this carefully written book and would certainly recommend it to postgraduate students and researchers of meteorology, and applied mathematicians and statisticians who deal with such environmental science data.” (Computing Reviews, October, 2017)