Premium
A Bayesian Markov Model of the flood forecast process
Author(s) -
Krzysztofowicz Roman
Publication year - 1983
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/wr019i006p01455
Subject(s) - flood myth , markov chain , bayesian probability , markov process , crest , markov decision process , computer science , mathematics , statistics , econometrics , geography , archaeology , physics , quantum mechanics
A flood forecast process with a discrete time index k is defined as { i(k), h(k) }, where i denotes the current flood level and h denotes the forecasted flood crest. It is a finite, random duration process with the actual flood crest hh being its terminal state: { hh | i(k) h(k) }. This process is modeled as a two‐branch Markov chain. Nonstationary transition probability functions are obtained from a Bayesian information processor which can be imbedded in a dynamic programing algorithm that solves an ensuing Markovian decision problem. The lead time and the processing time of the forecasts are represented by their certainty equivalents. The structure of the model is motivated by an analysis of historical flood forecast records. Methods of estimation of the prior probability and likelihood functions are described. The model is intended primarily as a component of a decision methodology for determining the economic value of and the optimal decisions in response to riverine flood forecasts.