By Shreeda Maskey
Like several normal risks, flooding is a fancy and inherently doubtful phenomenon. regardless of advances in constructing flood forecasting types and strategies, the uncertainty in forecasts continues to be unavoidable. This uncertainty has to be stated, and uncertainty estimation in flood forecasting offers a rational foundation for risk-based standards. This booklet provides the improvement and purposes of varied equipment in line with probablity and fuzzy set theories for modelling uncertainty in flood forecasting structures. particularly, it offers a technique for uncertainty evaluation utilizing disaggregation of time sequence inputs within the framework of either the Monte Carlo strategy and the bushy Extention Principle. It reviews an development within the First Order moment second process, utilizing moment measure reconstruction, and derives qualitative scales for the translation of qualitative uncertainty. program is to flood forecasting types for the Klodzko catchment in POland and the Loire River in France. customers for the hybrid suggestions of uncertainty modelling and probability-possibility changes also are explored and reported.
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Additional info for Modelling Uncertainty in Flood Forecasting Systems
Such assessments of model uncertainty however, remain largely subjective. The methods and methodologies developed and applied in this research are primarily focused on the treatment of input and parameter uncertainty. 2), in which all identified sources of uncertainty can be represented qualitatively. 3 Theories of uncertainty representation Historically, probability theory has been the primary tool for representing uncertainty in mathematical models (Ross, 1995). With the rapid development of computer technology and its use in mathematical modelling, the need of error representation in digital computation was increasingly realised in the 1950s and early 1960s, leading to the invention of interval arithmetic (Moore, 1962 & 1966).
At the end of this chapter, discussion is presented to illustrate some important distinctions and similarities of the uncertainty assessment methods presented at the beginning of the chapter. 1 Probability theory-based methods The assessment of uncertainty in a model output requires the propagation of different sources of uncertainty through the model. The probability theory-based uncertainty assessment methods involve either the propagation of probability distributions or the moments of distributions (means and variances).
Maskey (2001) and Maskey et al. (2002a) reported the method in the context of flood forecasting. The procedures involved in the application of this method are as follows: Identification of sources of uncertainty and decomposition As in any other methods of uncertainty analysis, the first step consists of identifying the sources of uncertainty in the output. These sources are then grouped into parameters and sub-parameters (decomposition of parameters). This allows the expert to assess the individual contributions of the decomposed parameters/sub-parameters rather than to assess the uncertainty of a combination of many parameters.
Modelling Uncertainty in Flood Forecasting Systems by Shreeda Maskey