Analysis and modelling of spatial environment data

著者

書誌事項

Analysis and modelling of spatial environment data

Mikhail Kanevski & Michel Maignan

EPFL Press, distributed by Marcel Dekker, c2004

  • : EPFL Press
  • : Marcel Dekker

大学図書館所蔵 件 / 全5件

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注記

"Environmental sciences"--T.p.

Includes bibliographical references (p. [275]-288)

内容説明・目次

内容説明

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.

目次

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

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