Analysis and Modelling of Spatial Environmental Data

TRAMA

Analysis and Modelling of Spatial Environment 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. This authoritative reference describes real case studies using Geostat Office software tools under MS Windows and includes timely chapters on monitoring network analysis, artificial neural networks, support vector machines, and simulations. The book also provides tools and methods to solve challenges in prediction, characterization, optimization, and density estimation.

NOTE EDITORE

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.

SOMMARIO

INTRODUCTION TO ENVIRONMENTAL DATA ANALYSIS AND MODELLINGIntroductionEnvironmental Decision Support Systems and Prediction MappingPresentation of the Case StudiesSpatial Data Analysis with Geostat OfficeEXPLORATORY SPATIAL DATA ANALYSIS, ANALYSIS OF MONITORING NETWORKS, AND DECLUSTERINGIntroductionExploratory Data AnalysisTransformation of DataQuantitative Description of Monitoring NetworksDeclusteringGeostat Office: Monitoring Networks and DeclusteringConclusionsSPATIAL DATA ANALYSIS: DETERMINISTIC INTERPOLATIONSIntroductionValidation ToolsModels of Deterministic InterpolationsDeterministic Interpolations with Geostat OfficeConclusionsINTRODUCTION TO GEOSTATISTICS: VARIOGRAPHYGeostatistics: Theory of Regionalized VariablesGeostatistics: Basic HypothesisVariographyCoregionilzation ModelsExploratory Variography in PracticeVariography with Geostat OfficeComments and InterpretationsConclusionGEOSTATISTICAL SPATIAL PREDICTIONSIntroductionFamily of Kriging ModelsKriging Predictions with Geostat OfficeSpatial Co-Estimations. Co-Kriging ModelsCo-Kriging Predictions. A Case StudyConclusionsESTIMATION OF LOCAL PROBABILITY DENSITY FUNCTIONSIntroductionIndicator KrigingIndicator Kriging. A Case StudyConclusions and Comments on Indicator KrigingCONDITIONAL STOCHASTIC SIMULATIONSIntroductionModels of Spatial SimulationsConditional Stochastic Simulations. Case StudiesReview of Other Simulation ModelsComments and DiscussionsCheck of the SimulationsConclusionsAnnex 1. Conditioning Simulations with Conditional KrigingAnnex 2. Non-Conditional Simulations of Stationary Isotropic Multiglasseian Random FunctionsAnnex 3. Sequential Guassian Simulations with Geostat OfficeARTIFICIAL NEURAL NETWORKS AND SPATIAL DATA ANALYSISIntroductionBasics of ANNArtificial Neural Networks LearningMultilayer Feedforward Neural NetworksGeneral Regression Neural Networks (GRNS)Neural Network Residual Kriging Model (NNRK)ConclusionsSUPPORT VECTOR MACHINES FOR ENVIRONMENTAL SPATIAL DATAIntroductionSupport Vector Machines ClassificationSpatial Data Mapping with Support Vector RegressionA Case StudyEvaluation of SVM Binary Spatial Classification with Nonparametric Conditional Stochastic SimulationsGeoSVM Computer ProgramConclusionsGEOGRAPHICAL INFORMATION SYSTEMS AND SPATIAL DATA ANALYSISIntroductionContributing Disciplines and TechnologiesGIS TechnologyGIS FunctionalityBasic Objects of GISRepresentation of the GIS ObjectGIS LayersMap ProjectionsGeostat Office and GISConclusionsCONCLUSIONSGLOSSARIESStatistics, Geostatistics, FractalsMachine LearningReferences

ALTRE INFORMAZIONI

• Condizione: Nuovo
• ISBN: 9780824759810
• Collana: Environmental Sciences
• Dimensioni: 11 x 8.5 in Ø 1.55 lb
• Formato: Copertina rigida
• Pagine Arabe: 300