Handling Missing Data
Applications to Environmental Analysis
Published on 18. November 2003
Book
Hardback
200 pages
978-1-85312-992-6 (ISBN)
Description
The management and remediation of environmental data time series have always been tiresome but necessary tasks as increasingly complex meteorological and dispersion models claim more and more data inputs. Featuring a wide range of techniques for analysing and filling gaps in time series data, this book contains recent research by the Air Pollution and Environmental Fluid Dynamics Group at Marche University of Technology, Ancona. The contributions may be viewed both as tools to manage practical air pollution problems and as a compendium of theoretical knowledge for a new understanding of lesser-known aspects.
More details
Series
Edition
Illustrated edition
Language
English
Place of publication
Southampton
United Kingdom
Target group
College/higher education
Professional and scholarly
Edition type
Illustrated edition
Illustrations
Ill.
Dimensions
Height: 230 mm
Width: 155 mm
ISBN-13
978-1-85312-992-6 (9781853129926)
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Schweitzer Classification
Content
Chapter 1: An introduction to the statistical filling of environmental data time series Classification of missing data; Linear interpolation; Imputation techniques Chapter 2: Data validation and data gaps in environmental time series Monitoring stations; Analysis and validation of time series Chapter 3: Statistical modelling of the remediation of environmental-data time series Statistical modelling applicable to time series; Autoregressive models and time series; Development of models for forecast and remediation Chapter 4: Imputation techniques for meteorological and air quality data filling Imputation techniques and missing data; Nearest Neighbour techniques; Spatial interpolation; The Voronoi diagram; Statistic filling of sparse time series Chapter 5: Neural Networks and their applications to meteorological and air quality data filling Introduction to Neural Networks; Neural Networks in data remediation; A survey on Neural Network applications in meteorological and air quality fields; Building up networks for remediation of time-series; Are the ANN applicable to the remediation of time series?