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Five empirical models, for prediction of peak discharge and estimation of flood



"A flood is an overflow of water on normally dry ground". This is most commonly due to an overflowing river, a dam break, snow-melt, or heavy rainfall. Tsunamis, storm surge, or coastal flooding are some of the examples of common flooding whereas instances of extreme flooding includes the flood  in 1931 at China which killed between 2,000,000 and 4,000,000 people.In 2002, Texas flood which was caused by a reservoir overflow. In just three days, the floodwaters carved canyons measuring 1.4 mile (2.2 kilometres) long and  20-foot-(6 meters)-deep.

Floods can be measured for height, peak discharge, area inundated, and volume of flow.

Due to the ability of this extreme event to cause disasters, it is important to predict the event well in advance or at the time of designing flood control structures.Flood is a result of a discharge;generally peak discharge;which the drainage network can not hold and eventually overflows and causes inundation. 

There are various methods to estimate peak discharge which acts as an indicator of flood.The methods are conceptual as well as empirical.The main difference between conceptual and empirical research is that conceptual research involves abstract ideas and concepts, whereas empirical research involves research based on observation, experiments and verifiable evidence.

Below are the five of the most popular and conventional empirical models which are used to estimate peak discharge.However the reliability of the model varies with change in location of river basins or watershed areas.The detail description of these methods can be found in my presentation on Flood.

1)Dickens Formula
2)Ryves Formula
3)Inglis Formula
4)Fullers Formula
5)Baird and Mcillwraith

Which one is best ?

That will depend on the location of its use.

How to use ?

Read my presentation on Flood.It has a detail description about these methods.

Which one is better : Conceptual,Empirical or the Contemporary Neural Network based models ?

The conceptual models is developed based on the causal relationship between a set of input variables and an output variable which is the peak discharge. Although this type of models are more reliable compared to the empirical models as its delineates the inherent concepts but it requires the data of numerous variables as peak discharge is a multi variate function and depends on many factors like rainfall,area of watershed,time of concentration,evaporation,transpiration,infiltration etc.But the empirical models are generally data driven models.This type of models are developed based on the available data of the watershed for which the model is being developed.It has less accuracy as it does not follow the inherent relation but tries to learn the concept from the data of input and output variables. The advantage of such method is its less data requirement as number of variables are generally less compared to the conceptual model.
Contemporary neural network models on the other hand depends on the data and is an empirical model.But its learning process is adaptive that means it can adjust itself with change in patterns of data which is observed when the location is changed.
The accuracy of this type of models are better compared to the other two type of models and number of variables is generally lesser compared to empirical models but it requires a large set of data to learn the problem.

Which type of models to select and how ?

Actually depending upon the availability of data of the related variables we must select a specific type of models.Also the objective of the model selection need to be clear.If it is for design purpose then the data requirement will be high and it is better to use conceptual modeled. But if there is no or very scarce data of some variables then it is better to use an empirical model with the variables having high number of data sets.If this model will be used in other watersheds also then it is better to utilize a neural network model(You can see my presentation on neural network to have an idea about the procedure to apply neural networks in prediction of peak discharge or any other variables.)

To select the best method as per your requirement of modeling you can use ODM tool.Following can be utilized as criteria :
1)Number of input variables to consider
2)Number of watersheds on which the model will be used
3)Availability of data for each variables
4)Accessibility of data for each variables
5)Desired Accuracy

If your priority is number of input variables to be considered then give highest score to criteria 1 .If data is limited then highest score need to be assigned to the Criteria 3. If you want to make a reliable and accurate models then the highest significance need to given to Criteria 5 and so on.

Use the type of models as the options and score them according to the selected criteria. The result which will be provided on click of the "Update" button, you will get the best variety of models to use for approximation of the peak discharge.This selection will be based on your preference and among the type of models used as options.
Thanking you,

Founding and Honorary Editor,

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