Accession Number:

ADA616937

Title:

Model Learning for Probabilistic Simulation on Rare Events and Scenarios

Descriptive Note:

Final rept. 3 Jun 2012-3 Jul 2015

Corporate Author:

OSAKA UNIV (JAPAN) INST OF SCIENTIFIC AND INDUSTRIAL RESEARCH

Personal Author(s):

• Washio, Takashi

Report Date:

2015-03-06

Pagination or Media Count:

15.0

Abstract:

This project established a new methodology for probabilistic inference and prediction of rare and special events andor 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 andor large scale system where a complete set of possible events and scenarios is hardly obtained.

Descriptors:

• *COMPUTERIZED SIMULATION
• *DATA MINING
• *KNOWLEDGE BASED SYSTEMS
• *STATISTICAL INFERENCE
• LEARNING MACHINES
• MARKOV PROCESSES
• MATHEMATICAL MODELS
• MONTE CARLO METHOD
• NATURAL DISASTERS
• PATTERN RECOGNITION
• PROBABILITY
• RISK ANALYSIS

Subject Categories:

• Statistics and Probability
• Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE