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Time Series Scenario Composition Framework in Supporting Environmental Simulation Tasks

Authors
Li, Chi-Yu (Chair of Environmental Informatics, Brandenburg University of Technology Cottbus-Senftenberg)
Search for Chi-Yu Li in Research Programmes Information System
Molkenthin, Frank (Chair of Environmental Informatics, Brandenburg University of Technology Cottbus-Senftenberg)
Search for Frank Molkenthin in Research Programmes Information System
Chapter Tools for Modeling and Simulation of Environmental Systems
Volume Proceedings of the 28th Conference on Environmental Informatics - Informatics for Environmental Protection, Sustainable Development and Risk Management
Conference EnviroInfo 2014 - ICT for Energy Effieciency
Oldenburg, 2014
Year 2014
Abstract of the Article
To answer the impacts under specific what-if scenarios together with simulation tools has been
demanding in different environmental problems. In this contribution, a general software
framework for time series scenario composition is proposed to deal with this issue. It is done
through providing an interface to process available raw time series data and to compose scenarios
of interest. These composed scenarios can be further converted to a set of time series data, e.g.
boundary conditions, for simulation tasks in order to investigate the impacts. This software
framework contains four modules: data pre-processing, event identification, process identification,
and scenario composition. These modules mainly involve Time Series Knowledge Ming (TSKM),
fuzzy logic and Multivariate Adaptive Regression Splines (MARS) to extract features from the
raw time series data and then interconnect them. These extracted features together with other
statistical information form the most basic elements, MetaEvents, for the semi-automatic scenario
composition. Besides, a software prototype with two application examples containing measured
hydrological and hydrodynamic data are used to demonstrate the benefit of the concept. The
results present the capability of reproducing similar time series patterns from specific scenarios
comparing to the original ones as well as the capability of generating new artificial time series data
from composed scenarios based on the interest of users for simulation tasks. Overall, the
framework provides an approach to fill the gap between raw data and simulation tools in
engineering suitable manner.
Pages 247 - 254
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