DeepAI AI Chat
Log In Sign Up

Comparison of annual maximum series and flood-type-differentiated mixture models of partial duration series

11/26/2021
by   Svenja Fischer, et al.
Ruhr University Bochum
0

The use of the annual maximum series for flood frequency analyses limits the considered information to one event per year and one sample that is assumed to be homogeneous. However, flood may have different generating processes, such as snowmelt, heavy rainfall or long-duration rainfall, which makes the assumption of homogeneity questionable. Flood types together with statistical flood-type-specific mixture models offer the possibility to consider the different flood-generating processes separately and therefore obtain homogeneous sub-samples. The combination of flood types in a mixture model then gives classical flood quantiles for given return periods. This higher flexibility comes to the cost of more distribution parameters, which may lead to a higher uncertainty in the estimation. This study compares the classical flood frequency models such as the annual maximum series with the type-specific mixture model for different scenarios relevant for design flood estimation in terms of Bias and variance. Thee results show that despite the higher number of parameters, the mixture model is preferable compared to the classical models, if a high number of flood events per year occurs and/or the flood types differ significantly in their distribution parameters.

READ FULL TEXT

page 9

page 10

page 12

page 17

page 18

07/01/2021

Robust Estimation in Finite Mixture Models

We observe a n-sample, the distribution of which is assumed to belong, o...
07/22/2022

Generalized Identifiability Bounds for Mixture Models with Grouped Samples

Recent work has shown that finite mixture models with m components are i...
10/04/2016

The Search Problem in Mixture Models

We consider the task of learning the parameters of a single component o...
06/23/2014

Exact fit of simple finite mixture models

How to forecast next year's portfolio-wide credit default rate based on ...
06/20/2019

Adversarial Self-Paced Learning for Mixture Models of Hawkes Processes

We propose a novel adversarial learning strategy for mixture models of H...
03/25/2020

Bayesian Hierarchical Bernoulli-Weibull Mixture Model for Extremely Rare Events

Estimating the duration of user behavior is a central concern for most i...