Cover Image
Read Now

Neural Nets: Applications in Geography

Neural nets offer a fascinating new strategy for spatial analysis, and their application holds enormous potential for the geographic sciences. However, the number of studies that have utilized these techniques is limited. This lack of interest can be attributed, in part, to lack of exposure, to the...

Full description

Bibliographic Details
Other Authors: Hewitson, B. (Editor), Crane, R.G. (Editor)
Format: eBook
Language:English
Published: Dordrecht Springer Netherlands 1994, 1994
Edition:1st ed. 1994
Series:GeoJournal Library
Subjects:
Complex Systems
Geography
Sociology / Methodology
System Theory
Mathematical Physics
Theoretical, Mathematical And Computational Physics
Sociological Methods
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • 6.4 Neural Spatiotemporal Forecasting: Qualified Conclusions
  • Seven - Precipitation Controls in Southern Mexico
  • 7.0 The Issue
  • 7.1 Southern Mexico Precipitation
  • 7.2 Climate Representation in the Data Set
  • 7.3 Neural Net Design and Training
  • 7.4 Neural Net Interpretation-Theory
  • 7.5 Neural Net Interpretation — Implementation
  • 7.6 Precipitation Onset
  • 7.7 Early Established Summer Rains
  • 7.8 Late Summer Precipitation Maximum
  • 7.9 Decay of the Summer Rains
  • 7.10 Conclusions
  • Eight - Classification of Arctic Cloud and Sea Ice Features in Multi-Spectral Satellite Data
  • 8.0 Introduction
  • 8.1 Cloud Detection and Classification
  • 8.2 Cloud Pattern Analysis Using Texture
  • 8.3 Discussion
  • 8.4 Other Neural Network Applications to Cloud Classification
  • 8.5 Sea Ice Fracture Patterns
  • 8.6 Neural Network Approach
  • 8.7 Conclusions
  • Appendix I - Neural Network Resources
  • Appendix II - Fortran 77 Listing for Kohonen Self Organizing Map
  • One — Looks and Uses
  • 1.0 Origins and Growth
  • 1.1 Conceptual Overview
  • 1.2 Neural Net Structures
  • 1.3 Implementing the Neural Net
  • 1.4 Inevitable Caveats and Cautions
  • 1.5 Where Next?
  • Two — Neural Networks and their Applications
  • 2.0 Introduction
  • 2.1 Neural Network Language and Basic Operation
  • 2.2 Multilayer Perceptrons and the Backpropagation of Error Algorithm
  • 2.3 Kohonen’s Self-Organizing Feature Maps
  • 2.4 Neural Networks and System Identification
  • 2.5 Areas of Current Research
  • Three — Neuro Classification of Spatial Data
  • 3.0 Towards a Computational Geography
  • 3.1 Whither Neuroclassification?
  • 3.2 Review of Potential Neuroclassifier Architectures
  • 3.3 Competitive Learning Nets
  • 3.4 Self-Organizing Map
  • 3.5 Adaptive Resonance Theory
  • 3.6 Associative Memory Nets
  • 3.7 Comparisons With Conventional Classifiers
  • 3.8 Kohonen’s Self-Organizing Map
  • 3.9 Conclusions
  • ChapterChapter Four — Self Organizing Maps — Application to Census Data
  • 4.0 South African Census Records
  • 4.1 Net Classification
  • 4.2 Interpretation of the Mapping Surface
  • 4.3 Interpretation of Regions in the Mapping —The ‘Black’ Population
  • 4.4 Spatial Distribution of the Mapping
  • 4.5 Conclusions
  • Five - Predicting Snowfall from Synoptic Circulation: A Comparison of Linear Regression and Neural Network Methodologies
  • 5.0 Introduction
  • 5.1 Data Preparation and Methodology
  • 5.2 Principal Component Analysis — 700 mb Data
  • 5.3 SNOTEL Data Preparation
  • 5.4 Stepwise Multiple Regression Analyses
  • 5.5 Five-Day Smoothed Results
  • 5.6 Neural Network Analysis
  • 5.7 Conclusions
  • Six - Neural Computing and the Aids Pandemic: The Case of Ohio
  • 6.0 The AIDS Pandemic, circa, 1993
  • 6.1 Spatiotemporal Neural Forecasting
  • 6.2 Neural Forecasting of the AIDSEpidemic
  • 6.3 A Sensitivity Analysis

Similar Items