Model Learning for Probabilistic Simulation on Rare Events and Scenarios

Abstract

This project established a new methodology for probabilistic inference and prediction of rare and special events and/or scenarios based on the simulation models and the observed data that rarely contain rate events, applied it to a rainfall flood risk analysis of Chikugo river, Japan, and showed that it can generate various rainfall scenario that causes a flood. Rainfall pattern that causes a flood is generated by Replica Exchange Monte Carlo algorithm, and covariant shift phenomenon was corrected by placing more weight on the flood region. This work gives a general framework to cope with the problem of handling a complex and/or large scale system where a complete set of possible events and scenarios is hardly obtained.

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Document Details

Document Type
Technical Report
Publication Date
Mar 06, 2015
Accession Number
ADA616937

Entities

People

  • Takashi Washio

Organizations

  • Osaka University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Algorithms
  • Big Data
  • Computational Science
  • Computations
  • Data Mining
  • Flood Control
  • Flood Hazards
  • Floods
  • Information Science
  • Learning
  • Monte Carlo Method
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Risk
  • Risk Analysis
  • Simulations

Fields of Study

  • Computer science

Readers

  • Coastal and Marine Engineering/Sediment Transport/Hydraulic Engineering
  • Computational Modeling and Simulation
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference