Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Modeling Probability of Blockage at Culvert Trash Screens Using Bayesian Approach
Trash screens are commonly installed at culvert entrances to prevent the ingress of debris that might otherwise become lodged within the structure. However, these can be a hazard in themselves if not cleared at an appropriate interval. There are currently no tools available to support making decisions regarding screen inspection requirements based upon potential site-by-site blockage risks. The analysis reported here was performed to address this issue. The parameter considered as key in the decision-making process was the probability of screen blockage. To determine this for any screen under consideration, a stochastic predictive model was developed using inspection records, obtained from 140 screens in Belfast, Northern Ireland, to relate blockage probabilities to seven potential drivers extracted from channel, land-use, meteorological, temporal, and social deprivation factors, employing a logistic regression approach. To allow for randomness in the data set, a Bayesian framework was adopted through which the uncertainty associated with any prediction could be reported using appropriate credible intervals. The predictive accuracy of the model was also assessed using appropriate measures and, despite documented uncertainties, was shown to be well within acceptable limits.
Modeling Probability of Blockage at Culvert Trash Screens Using Bayesian Approach
Trash screens are commonly installed at culvert entrances to prevent the ingress of debris that might otherwise become lodged within the structure. However, these can be a hazard in themselves if not cleared at an appropriate interval. There are currently no tools available to support making decisions regarding screen inspection requirements based upon potential site-by-site blockage risks. The analysis reported here was performed to address this issue. The parameter considered as key in the decision-making process was the probability of screen blockage. To determine this for any screen under consideration, a stochastic predictive model was developed using inspection records, obtained from 140 screens in Belfast, Northern Ireland, to relate blockage probabilities to seven potential drivers extracted from channel, land-use, meteorological, temporal, and social deprivation factors, employing a logistic regression approach. To allow for randomness in the data set, a Bayesian framework was adopted through which the uncertainty associated with any prediction could be reported using appropriate credible intervals. The predictive accuracy of the model was also assessed using appropriate measures and, despite documented uncertainties, was shown to be well within acceptable limits.
Modeling Probability of Blockage at Culvert Trash Screens Using Bayesian Approach
Streftaris, G. (Autor:in) / Wallerstein, N. P. (Autor:in) / Gibson, G. J. (Autor:in) / Arthur, S. (Autor:in)
Journal of Hydraulic Engineering ; 139 ; 716-726
21.12.2012
112013-01-01 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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