Cumulative frequency analysis is the analysis of the frequency of occurrence of values of a phenomenon less than a reference value. The phenomenon may be time- or space-dependent. Cumulative frequency is also called frequency of non-exceedance. Cumulative frequency analysis is performed to obtain insight into how often a certain phenomenon (feature) is below a certain value. This may help in describing or explaining a situation in which the phenomenon is involved, or in planning interventions, for example in flood protection. This statistical technique can be used to see how likely an event like a flood is going to happen again in the future, based on how often it happened in the past. It can be adapted to bring in things like climate change causing wetter winters and drier summers. Frequency analysis is the analysis of how often, or how frequently, an observed phenomenon occurs in a certain range. Frequency analysis applies to a record of length N of observed data X1, X2, X3 . . . XN on a variable phenomenon X. The record may be time-dependent (e.g. rainfall measured in one spot) or space-dependent (e.g. crop yields in an area) or otherwise. The cumulative frequency MXr of a reference value Xr is the frequency by which the observed values X are less than or equal to Xr. The relative cumulative frequency Fc can be calculated from: where N is the number of data Briefly this expression can be noted as: When Xr = Xmin, where Xmin is the unique minimum value observed, it is found that Fc = 1/N, because M = 1. On the other hand, when Xr = Xmax, where Xmax is the unique maximum value observed, it is found that Fc = 1, because M = N. Hence, when Fc = 1 this signifies that Xr is a value whereby all data are less than or equal to Xr. In percentage the equation reads: The cumulative probability Pc of X to be smaller than or equal to Xr can be estimated in several ways on the basis of the cumulative frequency M. One way is to use the relative cumulative frequency Fc as an estimate.

About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Related courses (2)
ENV-221: Hydrology for engineers
This is an introductory course to key concepts and methods in physical and engineering hydrology.
Related concepts (16)
Generalized extreme value distribution
In probability theory and statistics, the generalized extreme value (GEV) distribution is a family of continuous probability distributions developed within extreme value theory to combine the Gumbel, Fréchet and Weibull families also known as type I, II and III extreme value distributions. By the extreme value theorem the GEV distribution is the only possible limit distribution of properly normalized maxima of a sequence of independent and identically distributed random variables.
Log-logistic distribution
In probability and statistics, the log-logistic distribution (known as the Fisk distribution in economics) is a continuous probability distribution for a non-negative random variable. It is used in survival analysis as a parametric model for events whose rate increases initially and decreases later, as, for example, mortality rate from cancer following diagnosis or treatment. It has also been used in hydrology to model stream flow and precipitation, in economics as a simple model of the distribution of wealth or income, and in networking to model the transmission times of data considering both the network and the software.
Gumbel distribution
In probability theory and statistics, the Gumbel distribution (also known as the type-I generalized extreme value distribution) is used to model the distribution of the maximum (or the minimum) of a number of samples of various distributions. This distribution might be used to represent the distribution of the maximum level of a river in a particular year if there was a list of maximum values for the past ten years. It is useful in predicting the chance that an extreme earthquake, flood or other natural disaster will occur.
Show more

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.