Studies on time series applications in environmental sciences [electronic resource] /
Bărbulescu, Alina.author.
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.  .
Description based upon print version of record.
Preface; Contents; Data Series; 1 Hypotheses Testing on Meteorological Time Series; 1 Normality Tests; 2 Homoskedasticity Tests; 3 Autocorrelation Tests; 4 Outliers' Detection; 5 Change Points' Detection; 6 Testing for Long Range Dependence Property; 7 Goodness of Fit Tests; References; 2 Mathematical Methods Applied for Hydro-meteorological Time Series Modeling; 1 Types of Models. Classical Decomposition Method; 2 Box-Jenkins Approach and Stationarity Tests; 2.1 Box-Jenkins Approach; 2.2 Stationarity Tests; 3 Genetic Algorithms; 3.1 Gene Expression Programming
3.2 Adaptive Gene Expression Programming4 Support Vector Regression (SVR); 5 General Regression Neural Network (GRNN); 6 Wavelets; References; 3 Models for Precipitation Series; 1 ARMA Models for Precipitation Series and Generation of Precipitation Fields; 1.1 ARMA Models for Precipitation Series and Generation of Annual Precipitation Fields; 1.1.1 Generation of Annual Precipitation Series for the Main Stations in Dobrogea; 1.1.2 Models for Annual Precipitation Series from the Secondary Stations in Dobrogea; 1.2 Generation of Monthly Precipitation Series; 1.2.1 Theoretical Considerations
1.2.2 Case Study2 Modeling and Forecast Using GEP, AdaGEP, SVR, GRNN and Hybrid Models; 3 Decomposition and Wavelets Models; References; 4 Modeling the Precipitation Evolution at Regional Scale; 1 On the Correlation and Predictability of Precipitation at Regional Scale; 2 Modeling the Regional Precipitation; 2.1 Models for the Regional Annual and Monthly Precipitation Series; 2.2 Models for the Regional Monthly Maximum Annual Precipitation; 3 Generation of Annual Precipitation Field at Regional Scale; 3.1 Overview of Some Theoretical Approaches
3.2 Application to Annual Precipitation in Dobrogea RegionReferences; 5 Analysis and Models for Surface Water Quality; 1 On the Hydrochemical Properties of the Water of Techirghiol Lake; 2 On the Temperature of the Water of Techirghiol Lake; References; 6 Models for Pollutants Dissipation; 1 GRNN Models; 2 Linear Models; References; 7 Spatial Interpolation with Applications; Abstract; 1 Theoretical Considerations on the Spatial Interpolation Methods; 1.1 Mechanical Methods; 1.1.1 The Thiessen Polygons Method [27]; 1.1.2 Inverse Distance Weighted Interpolation (IDW)
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.  .
Computational intelligence.
Geotechnical engineering.
Artificial intelligence.
Computational Intelligence.
Geotechnical Engineering & Applied Earth Sciences.
Artificial Intelligence.