Earth Sciences - Papers & Sessions at the 2005 & 2006 IJCNNs

Starting with IJCNN 2005 in Montreal, Vladimir Cherkassky of the University of ?Minnesota? led an initiative to represent Earth Sciences applications of Computational Intelligence (CI), together with Co-Chairs Dmitri Solomatine, Vladimir Krasnopolsky and Julio Valdes. This effort was a huge success, and was carried into subsequent IJCNNs. The intent of this web-page is to provide a historical perspective on the papers and sessions related to the derivation and application of Computational Intelligence tools to Earth Sciences at the 2005 and 2006 IJCNNs, and to encourage authors to contribute and expand the range of topics addressed, and to provide insight into which CI tools work best, and what needs to be done to improve them for the future. A bit of a projection towards future sessions is also provided.

Note that many papers, especially those in poster sessions, and perhaps in the future this web-page will be better restructured to cluster similar topics in Earth Sciences together. Also, many relevant conference papers are missing from these lists, which focus mostly on special sessions. For example, several papers on satellite imagery and analysis are not included. Perhaps a more complete picture will be provided in the future.

Abstracts for papers should be added at a later date.

Subjects:

·         Flooding_Inland

·         Fundamental Mathematical and Statistical Toolsets

·         Ocean-Wind-Hurricane-Cyclone-Shoreline-Harbour

·         Predictive Uncertainty in Environmental Modelling

·         Satellite Data

·         Seismic

·         Solar-Climate

·         Tornados

·         Weather & Climate Prediction

·         ?Don't know...?

 


Conferences, Sessions, and Chairs

 

2006 IEEE World Congress on Computational Intelligence, Vancouver

Computational Intelligence in Earth & Environmental Sciences (Sessions I through III)

Chairs: Hsieh, Krasnopolsky, Solomatine, Valdes

Predictive Uncertainty in Environmental Modelling

Chair: Gavin Cawley

Temporal Data Analysis, Prediction & Forecasting

Chairs: Salvatore Marra and Francesco Morabito

 

2005 Applications of Learning and Data-Driven Methods to Earth Sciences and Climate Modeling (2 sessions)

Chair: Vladimir Cherkassky, Vladimir Krasnopolsky, Dimitri Solomatine, Julio Valdes


Papers by topic area

 

 

Fundamental Mathematical and Statistical Toolsets

 

Nonlinear Complex Principal Component Analysis and Its Applications

Sanjay Rattan, William Hsieh and Gerben Ruessink (IJCNN05)

University of British Columbia, Canada; Utrecht University, Netherlands

 

 

Solar-Climate

 

Solar Activity Forecasting by Incorporating Prior Knowledge from Nonlinear Dynamics into Neural Networks
Salvatore Marra and Francesco Morabito (IJCNN06)

 

Time Dependent Neural Network Models For Detecting Changes Of State In Earth and Planetary Processes

Julio J. Valdes and Graeme Bonham-Carter (IJCNN05)

National Research Council Canada, Canada; Geological Survey of Canada, Canada

 

 

Weather & Climate Prediction

 

Complex Hybrid Models Combining Deterministic and Machine Learning Components as a New Synergetic Paradigm in Numerical Climate Modeling and Weather Prediction

Vladimir Krasnopolsky and Michael Fox-Rabinovitz (IJCNN05)

Saic at Ncep/Noaa and Essic/Umd, United States; Essic/Umd, United States

 

Nonlinear Principal Predictor Analysis Using Neural Networks

Alex Cannon (IJCNN05), Meteorological Service of Canada, Canada

 

Temporal Neural Networks for Downscaling Climate Variability and Extremes

Yonas Dibike and Paulin Coulibaly (IJCNN05)

McMaster University, Canada

 

Semiblind source separation of climate data detects El Nino as the component with the highest interannual variability [#1459]

Alexander Ilin, Harri Valpola and Erkki Oja (IJCNN05)

Helsinki University of Technology, Finland

 

Intelligent systems for meteorological events forecast

Eros Pasero, Walter Moniaci and Tassilo Meindl (IJCNN05)

Polytechnic of Turin, Italy

 

Electric Load Forecasting using Scatter Search Based Weighted Average Weather Conditions
Masayuki Kobayashi, Tetsuya Yukawa, Yasuhito Kuze, Tetsuro Matsui and Tatsuya Iizaka (IJCNN06)

 

 

Ocean-Wind-Hurricane-Cyclone-Shoreline-Harbour

 

Using Neural Network to Enhance Assimilating Sea Surface Height Data into an Ocean Model
Vladimir Krasnopolsky, Carlos Lozano, Deanna Spindler, Ilya Rivin and Desiraju.B. Rao (IJCNN06)

 

Neural Network Modeling of Nearshore Sandbar Behavior
Leo Pape, Gerben Ruessink, Marco Wiering and Ian Turner (IJCNN06)

 

A Cross-wavelet Study of Alongshore Nonuniform Nearshore Sandbar Behavior
Gerben Ruessink, Giovanni Coco, Rosh Ranasinghe and Ian Turner (IJCNN06)

 

Application of Markov Chain Simulation for Model Calibration
Gerben Ruessink (IJCNN06)

 

Model Inversion by Parameter Fit Using NN Emulating the Forward Model - Evaluation of Indirect Measurements
Helmut Schiller (IJCNN06)

 

Tropical Cyclone Forecast Using Angle Features and Time Warping
James Liu, Bo Feng, Meng Wang and Weidong Luo (IJCNN06)

 

Atmospheric correction and oceanic constituents retrieval with a neuro-variational method

Julien Brajard, Cedric Jamet, Cyril Moulin and Sylvie Thiria

Locean/ipsl, France; University of British Columbia, Canada; Lsce/cea/ipsl, France (IJCNN05)

 

Modelling Harbour Sedimentation Using ANN and M5 Model Trees

Biswa Bhattacharya and Dimitri Solomatine (IJCNN05)

UNESCO-IHE Institute for Water Education, Delft, Netherlands

 

 

Flooding Inland

 

The Interval Estimation of Parameters for Back-Propagation Network to Flood Discharge Forecasting
Chang-Shian Chen, Chao-Chung Yang and Chin-Hui Liu (IJCNN06)

 

Streamflow Forecasting with Uncertainty Estimate Using Bayesian Learning for ANN

Mohammad Khan and Paulin Coulibaly (IJCNN05),  McMaster University, Canada

 

A Comparative Study of Artificial Neural Network Techniques for River Stage Forecasting

Christian Dawson, Linda See, Robert Abrahart, Robert Wilby and Asaad Shamseldin (IJCNN05)

Loughborough University, UK; University of Leeds, UK; University of Nottingham, UK; Environment Agency, UK; University of Auckland, New Zealand

 

Neural Network River Discharge Forecasters: An Empirical Investigation of Hidden Unit Processing Functions Based on Two Different Catchments

Asaad Shamseldin, Robert Abrahart and Linda See (IJCNN05)

University of Auckland, New Zealand; University of Nottingham, UK; University of Leeds, UK

 

Neural Network Modelling of the 20-Year Flood Event for 850 Catchments Across the UK

Christian Dawson, Robert Abrahart, Asaad Shamseldin, Robert Wilby and Linda See (IJCNN05)

Loughborough University, UK; University of Nottingham, UK; University of Auckland, New Zealand; Environment Agency, UK; University of Leeds, UK

 

 

Tornados

 

A Spatiotemporal Approach to Tornado Prediction [#1072]

Valliappa Lakshmanan, Indra Adrianto, Travis Smith and Greg Stumpf (IJCNN05)

University of Oklahoma and\\National Severe Stor, United States; University of Oklahoma, United States

 

 

Satellite Data

 

Applications of Neural Network Methods to the Processing of Earth Observation Satellite Data

Diego G. Loyola R. (IJCNN05),  German Aerospace Center (DLR), Germany

 

Robustness of the NN Approach to Emulating Atmospheric Long Wave Radiation in Complex Climate Models

Vladimir Krasnopolsky, Michael Fox-Rabinovitz and Ming-Dah Chou (IJCNN05)

Saic at Ncep/Noaa and Essic/Umd, United States; Essic/Umd, United States; National Taiwan University, Taiwan

 

Machine Learning in Soil Classification

Biswa Bhattacharya and Dimitri Solomatine (IJCNN05),  UNESCO-IHE Institute for Water Education, Delft, Netherlands

 

A multilayer perceptron approach for the retrieval of vertical temperature profiles from satellite radiation data

Elcio Hideiti Shiguemori, Jose Demisio Simoes da Silva, Haroldo Fraga Campos Velho and Joao Carlos Carvalho (IJCNN05)

 

 

Seismic

 

Classification of Infrasound Events Using Using Radial Basis Function Neural Networks

Fredric Ham, Kamel Rekab, Sungjin Park, Ranjan Acharyya and Young-Chan Lee (IJCNN05)

Florida Institute of Technology, United States; LG Mobile Handset Research Center, Korea (South)

 

 

Predictive Uncertainty in Environmental Modelling

 

A Variational EM Approach to Predicting Uncertainty in Supervised Learning
Markus Harva (IJCNN06)

 

Predictive Uncertainty in Environmental Modelling
Gavin Cawley, Malcolm Haylock and Stephen Dorling (IJCNN06)

 

Variance Stabilizing Regression Ensembles for Environmental Models
Anthony Bagnall, Ian Whittley, Matthew Studley, Mike Pettipher and Firat Tekiner (IJCNN06)

 

Ensemble of Competitive Associative Nets and Multiple K-fold Cross-Validation for Estimating Predictive Uncertainity in Environmental Modelling
Shuichi Kurogi, Daisuke Kuwahara and Shinya Tanaka (IJCNN06)

 

Generic Prioritization Framework For Target Selection And Instrument Usage For Reconnaissance Mission Autonomy
Fink Wolfgang (IJCNN06)

 

Deterministic Models and Neural Nets: A Successful Methodology for the Air Dispersion Models
Armando Pelliccioni, Tiziano Tirabassi, Sabrina Bellantone and Claudio Gariazzo (IJCNN06)

 

 

?Don't know... – haven't checked the classification…?

 

Local and Hybrid Learning Models in Forecasting Natural Phenomena

Dimitri Solomatine (IJCNN05),  UNESCO-IHE Institute for Water Education, Delft, Netherlands

 

Recurrent Neural Network Based Gating for Natural Gas Load Prediction System
Petr Musiek, Emil Pelikan, Tomas Brabec and Milan Simunek (IJCNN06)

 

Hybrid Model with Dynamic Architecture for Forecasting Time Series
Gecynalda Soares S. Gomes, Andre Luis S. Maia, Teresa B. Ludermir, Francisco de A.T. De Carvalho and Aluizio F. R. Araujo (IJCNN06)

 

These are probably flooding-related papers…

Estimation of Prediction Intervals for the Model Outputs using Machine Learning

D. L. Shrestha and Dimitri P. Solomatine (IJCNN05)

UNESCO-IHE Institute for Water Education, Netherlands; UNESCO-IHE Institute for Water Education, Delft, Netherlands

 

Issues in Designing Automated Minimal Resource Allocation Neural Networks

Momcilo Markus (IJCNN05), Illinois State Water Survey, Champaign, Illinois, United States

 

Wavelet Networks: An Alternative to Classical Neural Networks

Kamban Parasuraman and Amin Elshorbagy (IJCNN05)

University of Saskatchewan, Canada

 

 

These will be removed from the list…

 

Nonlinear System Identification Based on B-Spline Neural Network and Modified Particle Swarm Optimization
Leandro dos Santos Coelho and Renato Krohling (IJCNN06)