2006 Special issue: Computational intelligence in earth sciences and environmental applications: Issues and challenges

Authors:
V. Cherkassky;V. Krasnopolsky;D. P. Solomatine;J. Valdes
Affiliations:
Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA;SAIC, EMC/NCEP/NOAA, Camp Springs, MD, USA and Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA;UNESCO-IHE Institute for Water Education, Delft, The Netherlands;National Research Council, Institute for Information Technology, Montreal, Canada
Venue:
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
Year:
2006

Citing 9
Cited 3

Neural computing: an introduction

Neural computing: an introduction
The nature of statistical learning theory

The nature of statistical learning theory
Data preparation for data mining

Data preparation for data mining
Bayesian Learning for Neural Networks

Bayesian Learning for Neural Networks
Learning from Data: Concepts, Theory, and Methods

Learning from Data: Concepts, Theory, and Methods
Neural network uncertainty assessment using Bayesian statistics: a remote sensing application

Neural Computation
Some neural network applications in environmental sciences. Part I: forward and inverse problems in geophysical remote measurements

Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
Some neural network applications in environmental sciences. Part II: advancing computational efficiency of environmental numerical models

Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
Multiple model regression estimation

IEEE Transactions on Neural Networks

Risk assessment modelling of microbiology-related solids separation problems in activated sludge systems

Environmental Modelling & Software
Feature extraction using circular statistics applied to volcano monitoring

CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Semi-physical neural modeling for linear signal restoration

Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper introduces a generic theoretical framework for predictive learning, and relates it to data-driven and learning applications in earth and environmental sciences. The issues of data quality, selection of the error function, incorporation of the predictive learning methods into the existing modeling frameworks, expert knowledge, model uncertainty, and other application@?domain specific problems are discussed. A brief overview of the papers in the Special Issue is provided, followed by discussion of open issues and directions for future research.