A Bayesian approach to analysing structural uncertainties in flood inundation models

The novelty of the research project reported in this paper is the coupling of hydrological and hydraulic modeling which are based on the first principal of fluid mechanics for the simulation of flash floods at Wadi Elarish watershed to optimize the a new location of another dam rather than Elrawfa dam which already exist. Results show that, the optimum scenario is obtained by the construction of the west dam. As a direct result of this dam, the downstream inundated area can be reduced up to 15.7 % as function of reservoir available storage behind the dam. Furthermore, calculations showed that the reduction rate of inundated area for 50-year floods is largely more than 100-year floods, implies the high ability of west dam on flood control especially for floods with shorter return period.

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Verbal statistical learning: Prior knowledge impacts word identification but not metacognition

10.31234/osf.io/uk2dr ◽

2020 ◽

Author(s):

Laetitia Zmuda ◽

Charlotte Baey ◽

Paolo Mairano ◽

Anahita Basirat

Keyword(s):

Task Performance ◽

Prior Knowledge ◽

Statistical Learning ◽

Cognitive Processes ◽

Bayesian Approach ◽

Word Identification ◽

Artificial Language ◽

Artificial Languages ◽

Transitional Probabilities ◽

Fully Bayesian

It is well-known that individuals can identify novel words in a stream of an artificial language using statistical dependencies. While underlying computations are thought to be similar from one stream to another (e.g. transitional probabilities between syllables), performance are not similar. According to the “linguistic entrenchment” hypothesis, this would be due to the fact that individuals have some prior knowledge regarding co-occurrences of elements in speech which intervene during verbal statistical learning. The focus of previous studies was on task performance. The goal of the current study is to examine the extent to which prior knowledge impacts metacognition (i.e. ability to evaluate one’s own cognitive processes). Participants were exposed to two different artificial languages. Using a fully Bayesian approach, we estimated an unbiased measure of metacognitive efficiency and compared the two languages in terms of task performance and metacognition. While task performance was higher in one of the languages, the metacognitive efficiency was similar in both languages. In addition, a model assuming no correlation between the two languages better accounted for our results compared to a model where correlations were introduced. We discuss the implications of our findings regarding the computations which underlie the interaction between input and prior knowledge during verbal statistical learning.

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