期刊
HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES
卷 61, 期 2, 页码 225-244出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/02626667.2015.1016950
关键词
model fitting; parsimony; simulation; model identification; stochastics; entropy
资金
- Greek General Secretariat for Research and Technology through the research project Combined REnewable Systems for Sustainable Energy DevelOpment (CRESSENDO) [5145]
The old principle of parsimonious modelling of natural processes has regained its importance in the last few years. The inevitability of uncertainty and risk, and the value of stochastic modelling in dealing with them, are also again appreciated, after a period of growing hopes for radical reduction of uncertainty. Yet, in stochastic modelling of natural processes several families of models are used that are often non-parsimonious, unnatural or artificial, theoretically unjustified and, eventually, unnecessary. Here we develop a general methodology for more theoretically justified stochastic processes, which evolve in continuous time and stem from maximum entropy production considerations. The discrete-time properties thereof are theoretically derived from the continuous-time ones and a general simulation methodology in discrete time is built, which explicitly handles the effects of discretization and truncation. Some additional modelling issues are discussed with a focus on model identification and fitting, which are often made using inappropriate methods. [GRAPHICS]
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