Environmental informatics - Long-lead flood forecasting using Bayesian neural networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Neural Networks (NNs) are especially useful in exploratory data analysis to uncover and, or elucidate empirical relationships among data. Parameter estimation, the so-called "training" of neural networks is a variation of standard maximum likelihood estimation, whereby the optimal set of model parameters (the NN weights) maximizes the fit to the calibration (training) data set In our previous applications of neural networks in hydrometeorology, we focused on the development of complex architectures of neural networks adapted to the characteristics of the available data (multisensor, multiresolution mix of ground-based and satellite observations). These architectures consist of large structures of simpler networks built to embody clearly defined hypothesis of functional relationships that are consistent with the underlying physical processes (rainfall and flood forecasting, wind, temperature and moisture profiles in the atmosphere, temporal evolution of cloud and storm morphologies). One challenge we have not addressed previously is how to quantify the uncertainty in NN-based forecasts or estimates. We begin to address this question through the use of Bayesian Neural Networks (BNNs) for long-lead flood forecasting (18-hours).

Original languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks, IJCNN 2005
Pages3133-3137
Number of pages5
DOIs
StatePublished - 2005
Externally publishedYes
EventInternational Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada
Duration: Jul 31 2005Aug 4 2005

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume5

Other

OtherInternational Joint Conference on Neural Networks, IJCNN 2005
Country/TerritoryCanada
CityMontreal, QC
Period7/31/058/4/05

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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