A Bayesian-SWMM coupled stochastic model developed to reconstruct the complete profile of an unknown discharging incidence in sewer networks

In order to evaluate the feasibility of installing decentralised installations for wastewater reuse in cities, information about flows at specific spots of a sewer is needed. However, measuring intermittent flows in partially filled conduits is a technical task which is sometimes difficult to accomplish. This paper describes a method to model intermittent discharges in small sewers by linking a stochastic model for wastewater discharge to a hydraulic model to predict the attenuation of the discharges and its impact on the arrival time to a defined spot. The method was validated in a case study. The model estimated adequately the wastewater discharges on working days.

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A Fruitful Stochastic Model

Contemporary Psychology ◽

10.1037/007600 ◽

1964 ◽

Vol 9 (7) ◽

pp. 273-276

Author(s):

ANATOL RAPOPORT

Keyword(s):

Stochastic Model

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Some thoughts on "A stochastic model for individual choice behaviour"

PsycEXTRA Dataset ◽

10.1037/e627282012-097 ◽

1960 ◽

Author(s):

R. J. Audley

Keyword(s):

Stochastic Model ◽

Individual Choice ◽

Choice Behaviour

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A Stochastic Model for Evolution of Altruistic Genes

Journal de Physique I ◽

10.1051/jp1:1996223 ◽

1996 ◽

Vol 6 (4) ◽

pp. 445-453 ◽

Cited By ~ 4

Author(s):

Roberta Donato

Keyword(s):

Stochastic Model

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A Stochastic Model for the Inheritance of the Cancer Proneness Phenotype

Methods of Information in Medicine ◽

10.1055/s-0038-1636689 ◽

1987 ◽

Vol 26 (03) ◽

pp. 117-123

Author(s):

P. Tautu ◽

G. Wagner

Keyword(s):

Stochastic Model ◽

Gaussian Process ◽

Covariance Function ◽

Neoplastic Disease ◽

Disease Phenotype ◽

Probabilistic Representation ◽

Temporal Behaviour ◽

Continuous Trait ◽

Continuous Parameter ◽

Level Zero

SummaryA continuous parameter, stationary Gaussian process is introduced as a first approach to the probabilistic representation of the phenotype inheritance process. With some specific assumptions about the components of the covariance function, it may describe the temporal behaviour of the “cancer-proneness phenotype” (CPF) as a quantitative continuous trait. Upcrossing a fixed level (“threshold”) u and reaching level zero are the extremes of the Gaussian process considered; it is assumed that they might be interpreted as the transformation of CPF into a “neoplastic disease phenotype” or as the non-proneness to cancer, respectively.

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