Multivariate Statistical Methods for Assessment of Groundwater Chemistry Between the Waingawa and Waiohine Rivers, Wairarapa Valley PDF Download
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Author: Christopher John Daughney
Publisher:
ISBN:
Category : Groundwater
Languages : en
Pages : 94
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Book Description
Author: Christopher John Daughney
Publisher:
ISBN:
Category : Groundwater
Languages : en
Pages : 94
Get Book Here
Book Description
Author: Christopher John Daughney
Publisher:
ISBN:
Category : Groundwater
Languages : en
Pages : 80
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Book Description
Author: D.R. Helsel
Publisher: Elsevier
ISBN: 0080875084
Category : Science
Languages : en
Pages : 539
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Book Description
Data on water quality and other environmental issues are being collected at an ever-increasing rate. In the past, however, the techniques used by scientists to interpret this data have not progressed as quickly. This is a book of modern statistical methods for analysis of practical problems in water quality and water resources.The last fifteen years have seen major advances in the fields of exploratory data analysis (EDA) and robust statistical methods. The 'real-life' characteristics of environmental data tend to drive analysis towards the use of these methods. These advances are presented in a practical and relevant format. Alternate methods are compared, highlighting the strengths and weaknesses of each as applied to environmental data. Techniques for trend analysis and dealing with water below the detection limit are topics covered, which are of great interest to consultants in water-quality and hydrology, scientists in state, provincial and federal water resources, and geological survey agencies.The practising water resources scientist will find the worked examples using actual field data from case studies of environmental problems, of real value. Exercises at the end of each chapter enable the mechanics of the methodological process to be fully understood, with data sets included on diskette for easy use. The result is a book that is both up-to-date and immediately relevant to ongoing work in the environmental and water sciences.
Author: Christopher John Daughney
Publisher:
ISBN: 9780478196801
Category : Groundwater
Languages : en
Pages : 29
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Book Description
Author: Hemant Pathak
Publisher: LAP Lambert Academic Publishing
ISBN: 9783845423678
Category :
Languages : en
Pages : 60
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Book Description
This book investigates the water pollution due to Water-soluble chemical waste by multivariate statistical analysis. Also explains the different statistical methods for water quality assessment, including latest concepts and developments in the field with global and user-friendly approach. This features a systematic introduction to the types of water resources, contamination due to waste, origin of water pollutants, their effects on the environment and methods available to measure them. It also suggests statistical principles for controlling and monitoring of pollution-causing chemical waste in reference to highly populated city. Statistical Model capable to be formulated for estimating the location wise pollution load generation and pollution level forecasting and provide as a decision support system for better management of water resources and control of water pollution. The overall variation in the chemical parameters has been demonstrated in form of a seasonal assessment for different samples. Results were given by Complete statistical evaluation containing descriptive statistics, Correlation matrix, Principal Component Analysis, Factor analysis and Cluster analysis.
Author: Joshua F. Valder
Publisher:
ISBN:
Category :
Languages : en
Pages : 276
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Book Description
Multivariate statistical analysis has widely been applied in scientific work; however, its uses have been limited in scope when applied to hydrogeologic studies. Most current methods and approaches require one of two parameters: the mixing proportions, or the source area where the contribution is being supplied. In most studies, one of these parameters is known; however, a need exists in studies where neither of these parameters is known. A newly developed approach is presented and documented with the use of synthetic data sets. Two field tests are then shown. The first involved a study of the Madison aquifer at Wind Cave National Park in the southern Black Hills of South Dakota. The second test of the approach involved a study of the Cheyenne River.
Author: Andrew Stephan Gamble
Publisher:
ISBN:
Category : Cluster analysis
Languages : en
Pages : 750
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Book Description
This research performs a comparative study of techniques for combining spatial data and multivariate statistical methods for characterizing water quality conditions in a river basin. The study has been performed on the White River basin in central Indiana, and uses sixteen physical and chemical water quality parameters collected from 44 different monitoring sites, along with various spatial data related to land use - land cover, soil characteristics, terrain characteristics, eco-regions, etc. Various parameters related to the spatial data were analyzed using ArcHydro tools and were included in the multivariate analysis methods for the purpose of creating classification equations that relate spatial and spatio-temporal attributes of the watershed to water quality data at monitoring stations. The study compares the use of various statistical estimates (mean, geometric mean, trimmed mean, and median) of monitored water quality variables to represent annual and seasonal water quality conditions. The relationship between these estimates and the spatial data is then modeled via linear and non-linear multivariate methods. The linear statistical multivariate method uses a combination of principal component analysis, cluster analysis, and discriminant analysis, whereas the non-linear multivariate method uses a combination of Kohonen Self-Organizing Maps, Cluster Analysis, and Support Vector Machines. The final models were tested with recent and independent data collected from stations in the Eagle Creek watershed, within the White River basin. In 6 out of 20 models the Support Vector Machine more accurately classified the Eagle Creek stations, and in 2 out of 20 models the Linear Discriminant Analysis model achieved better results. Neither the linear or non-linear models had an apparent advantage for the remaining 12 models. This research provides an insight into the variability and uncertainty in the interpretation of the various statistical estimates and statistical models, when water quality monitoring data is combined with spatial data for characterizing general spatial and spatio-temporal trends.
