Long-Range Dependence and Sea Level Forecasting (SpringerBriefs in Statistics) - Tapa blanda

Ercan, Ali

 
9783319015040: Long-Range Dependence and Sea Level Forecasting (SpringerBriefs in Statistics)
Tapa blanda
ISBN 10: 3319015044 ISBN 13: 9783319015040
Editorial: Springer, 2013

Sinopsis

​This study shows that the Caspian Sea level time series possess long range dependence even after removing linear trends, based on analyses of the Hurst statistic, the sample autocorrelation functions, and the periodogram of the series. Forecasting performance of ARMA, ARIMA, ARFIMA and Trend Line-ARFIMA (TL-ARFIMA) combination models are investigated. The forecast confidence bands and the forecast updating methodology, provided for ARIMA models in the literature, are modified for the ARFIMA models. Sample autocorrelation functions are utilized to estimate the differencing lengths of the ARFIMA models. The confidence bands of the forecasts are estimated using the probability densities of the residuals without assuming a known distribution.

There are no long-term sea level records for the region of Peninsular Malaysia and Malaysia’s Sabah-Sarawak northern region of Borneo Island. In such cases the Global Climate Model (GCM) projections for the 21st century can be downscaled to the Malaysia region by means of regression techniques, utilizing the short records of satellite altimeters in this region against the GCM projections during a mutual observation period.

This book will be useful for engineers and researchers working in the areas of applied statistics, climate change, sea level change, time series analysis, applied earth sciences, and nonlinear dynamics.

"Sinopsis" puede pertenecer a otra edición de este libro.

Acerca del autor

Dr. Ali Ercan is a postdoctoral researcher at the University of California, Davis, Department of Civil and Environmental Engineering and co-manager of UC Davis J. Amorocho Hydraulics Laboratory since 2009. Dr. Ercan specializes in the areas of experimental and environmental hydraulics, sediment transport and water quality modelling, computational fluid dynamics, stochastic processes, and time series analysis.

Dr. Rovshan K. Abbasov is an Associate Professor at Khazar University and a laboratory head of the Hydrometeorology Institute of the Ministry of Environment and Natural Resources, Azerbaijan. He has long experience in research and consulting. His research interests are mainly focused on Integrated Water Resources Management, Watershed Hydrology, Flood Management, Disaster Risk Reduction and Eco-hydrology. Prof. M. Levent Kavvas is an endowed chair professor at the University of California, Davis, Department of Civil and Environmental Engineering, working in the areas of hydrology, hydraulics and hydro-climatology.

Prof. Kavvas has been the director of UC Davis J. Amorocho Hydraulics Laboratory since 1993. Prof. Kavvas is currently a member of the UNESCO Expert Group on Climate Change, member of Asia-Pacific Water Forum Steering Group on Climate Change and Water, consultant to Asian Development Bank on climate-change-related modeling studies, chair of American Society of Civil Engineers Hydro-climate committee, and member of the Climate Change Technical Advisory Group for California Department of Water Resources.

De la contraportada

?This study shows that the Caspian Sea level time series possess long range dependence even after removing linear trends, based on analyses of the Hurst statistic, the sample autocorrelation functions, and the periodogram of the series. Forecasting performance of ARMA, ARIMA, ARFIMA and Trend Line-ARFIMA (TL-ARFIMA) combination models are investigated. The forecast confidence bands and the forecast updating methodology, provided for ARIMA models in the literature, are modified for the ARFIMA models. Sample autocorrelation functions are utilized to estimate the differencing lengths of the ARFIMA models. The confidence bands of the forecasts are estimated using the probability densities of the residuals without assuming a known distribution.

There are no long-term sea level records for the region of Peninsular Malaysia and Malaysia s Sabah-Sarawak northern region of Borneo Island. In such cases the Global Climate Model (GCM) projections for the 21st century can be downscaled to the Malaysia region by means of regression techniques, utilizing the short records of satellite altimeters in this region against the GCM projections during a mutual observation period.

This book will be useful for engineers and researchers working in the areas of applied statistics, climate change, sea level change, time series analysis, applied earth sciences, and nonlinear dynamics.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2013
Idioma
Inglés
ISBN 10 
3319015044
ISBN 13 
9783319015040
Encuadernación
Tapa blanda
Número de páginas
60
Contacto del fabricante
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