Analysis and Modelling of Spatial Environmental Data: 1st Edition (Hardback) book cover

Analysis and Modelling of Spatial Environmental Data

1st Edition

By Mikhail Kanevski, Michel Maignan

EPFL Press

300 pages

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Hardback: 9780824759810
pub: 2004-03-30
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Description

Analysis and Modelling of Spatial Environmental Data presents traditional geostatistics methods for variography and spatial predictions, approaches to conditional stochastic simulation and local probability distribution function estimation, and select aspects of Geographical Information Systems. It includes real case studies using Geostat Office software tools under MS Windows and also provides tools and methods to solve problems in prediction, characterization, optimization, and density estimation. The author describes fundamental methodological aspects of the analysis and modelling of spatially distributed data and the application by way of a specific and user-friendly software, GSO Geostat Office.

Presenting complete coverage of geostatistics and machine learning algorithms, the book explores the relationships and complementary nature of both approaches and illustrates them with environmental and pollution data. The book includes introductory chapters on machine learning, artificial neural networks of different architectures, and support vector machines algorithms. Several chapters cover monitoring network analysis, artificial neural networks, support vector machines, and simulations. The book demonstrates thepromising results of the application of SVM to environmental and pollution data.

Table of Contents

INTRODUCTION TO ENVIRONMENTAL DATA ANALYSIS AND MODELLING

Introduction

Environmental Decision Support Systems and Prediction Mapping

Presentation of the Case Studies

Spatial Data Analysis with Geostat Office

EXPLORATORY SPATIAL DATA ANALYSIS, ANALYSIS OF MONITORING NETWORKS, AND DECLUSTERING

Introduction

Exploratory Data Analysis

Transformation of Data

Quantitative Description of Monitoring Networks

Declustering

Geostat Office: Monitoring Networks and Declustering

Conclusions

SPATIAL DATA ANALYSIS: DETERMINISTIC INTERPOLATIONS

Introduction

Validation Tools

Models of Deterministic Interpolations

Deterministic Interpolations with Geostat Office

Conclusions

INTRODUCTION TO GEOSTATISTICS: VARIOGRAPHY

Geostatistics: Theory of Regionalized Variables

Geostatistics: Basic Hypothesis

Variography

Coregionilzation Models

Exploratory Variography in Practice

Variography with Geostat Office

Comments and Interpretations

Conclusion

GEOSTATISTICAL SPATIAL PREDICTIONS

Introduction

Family of Kriging Models

Kriging Predictions with Geostat Office

Spatial Co-Estimations. Co-Kriging Models

Co-Kriging Predictions. A Case Study

Conclusions

ESTIMATION OF LOCAL PROBABILITY DENSITY FUNCTIONS

Introduction

Indicator Kriging

Indicator Kriging. A Case Study

Conclusions and Comments on Indicator Kriging

CONDITIONAL STOCHASTIC SIMULATIONS

Introduction

Models of Spatial Simulations

Conditional Stochastic Simulations. Case Studies

Review of Other Simulation Models

Comments and Discussions

Check of the Simulations

Conclusions

Annex 1. Conditioning Simulations with Conditional Kriging

Annex 2. Non-Conditional Simulations of Stationary Isotropic Multiglasseian Random Functions

Annex 3. Sequential Guassian Simulations with Geostat Office

ARTIFICIAL NEURAL NETWORKS AND SPATIAL DATA ANALYSIS

Introduction

Basics of ANN

Artificial Neural Networks Learning

Multilayer Feedforward Neural Networks

General Regression Neural Networks (GRNS)

Neural Network Residual Kriging Model (NNRK)

Conclusions

SUPPORT VECTOR MACHINES FOR ENVIRONMENTAL SPATIAL DATA

Introduction

Support Vector Machines Classification

Spatial Data Mapping with Support Vector Regression

A Case Study

Evaluation of SVM Binary Spatial Classification with Nonparametric Conditional Stochastic Simulations

GeoSVM Computer Program

Conclusions

GEOGRAPHICAL INFORMATION SYSTEMS AND SPATIAL DATA ANALYSIS

Introduction

Contributing Disciplines and Technologies

GIS Technology

GIS Functionality

Basic Objects of GIS

Representation of the GIS Object

GIS Layers

Map Projections

Geostat Office and GIS

Conclusions

CONCLUSIONS

GLOSSARIES

Statistics, Geostatistics, Fractals

Machine Learning

References

Subject Categories

BISAC Subject Codes/Headings:
MAT029000
MATHEMATICS / Probability & Statistics / General
TEC036000
TECHNOLOGY & ENGINEERING / Remote Sensing & Geographic Information Systems