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Machine Learning for Spatial Environmental Data: Theory, Applications, and Software
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TLDR
PREFACE LEARNING FROM GEOSPATIAL DATA Problems and important concepts of machine learning Machine learning algorithms for geospatial data Contents of the book Software description Short review of the literatureAbstract:
PREFACE LEARNING FROM GEOSPATIAL DATA Problems and important concepts of machine learning Machine learning algorithms for geospatial data Contents of the book Software description Short review of the literature EXPLORATORY SPATIAL DATA ANALYSIS PRESENTATION OF DATA AND CASE STUDIES Exploratory spatial data analysis Data pre-processing Spatial correlations: Variography Presentation of data k-Nearest neighbours algorithm: a benchmark model for regression and classification Conclusions to chapter GEOSTATISTICS Spatial predictions Geostatistical conditional simulations Spatial classification Software Conclusions ARTIFICIAL NEURAL NETWORKS Introduction Radial basis function neural networks General regression neural networks Probabilistic neural networks Self-organising maps Gaussian mixture models and mixture density network Conclusions SUPPORT VECTOR MACHINES AND KERNEL METHODS Introduction to statistical learning theory Support vector classification Spatial data classification with SVM Support vector regression Advanced topics in kernel methods REFERENCES INDEXread more
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Journal ArticleDOI
Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS
TL;DR: In this article, the authors proposed an ensemble weight-of-evidence (WoE) and support vector machine (SVM) model to assess the impact of classes of each conditioning factor on flooding through bivariate statistical analysis.
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Emerging artificial intelligence methods in structural engineering
Hadi Salehi,Rigoberto Burgueño +1 more
TL;DR: Techniques concerning applications of the noted AI methods in structural engineering developed over the last decade are summarized.
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Landslide susceptibility assessment using SVM machine learning algorithm
TL;DR: This paper introduces the current machine learning approach to solving spatial modeling problems in the domain of landslide susceptibility assessment, and selected Support Vector Machines as the model of choice to be compared with a common knowledge-driven method – the Analytical Hierarchy Process.
Journal ArticleDOI
Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information
TL;DR: This work identifies Random Forests as a good first choice algorithm for the supervised classification of lithology using remotely sensed geophysical data and indicates that as training data becomes increasingly dispersed across the region under investigation, MLA predictive accuracy improves dramatically.
Journal ArticleDOI
Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges
Songnian Li,Suzana Dragićević,Francesc Antón Castro,Monika Sester,Stephan Winter,Arzu Çöltekin,Christopher Pettit,Bin Jiang,James Haworth,Alfred Stein,Tao Cheng +10 more
TL;DR: The International Society for Photogrammetry and Remote Sensing (ISPRS) Technical Commission II (TC II) revisited the existing geospatial data handling methods and theories.