Analysis and Modelling of Spatial Environmental Data

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Citing examples that make use of real environmental spatial data, this book authoritatively presents traditional geostatistics methods for variography and spatial predictions, approaches to conditional stochastic simulation and local probability distribution function estimation, adaptive machine learning algorithms, and select aspects of Geographical Information Systems.

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Product Details

  • ISBN-13: 9780824759810
  • Publisher: Taylor & Francis
  • Publication date: 3/30/2004
  • Edition description: New Edition
  • Pages: 300
  • Product dimensions: 6.60 (w) x 9.70 (h) x 0.90 (d)

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)

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


GLOSSARIES Statistics, Geostatistics, Fractals Machine Learning References

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