Alternatively, if there any test to check if the model was successful in simulating the flow rates for the three storms?

The table of results has been attached.

Thanks in advance for all your help!

Thanks TheEcologist for your help.

Deb

- Thread starter debashishgoswami
- Start date

Alternatively, if there any test to check if the model was successful in simulating the flow rates for the three storms?

The table of results has been attached.

Thanks in advance for all your help!

Thanks TheEcologist for your help.

Deb

Alternatively, if there any test to check if the model was successful in simulating the flow rates for the three storms?

The table of results has been attached.

Thanks in advance for all your help!

Thanks TheEcologist for your help.

Deb

I'm glad you chose to start a thread this, I'll start off with my advice in our PM.

me said:

Regarding your problem:

You can use normal inferential/descriptive statistics to test your simulation results. People often do this, especially when your model has stochastic components you often have no choice. I cant give you any more detailed advice as you haven’t supplied a description of how your (simulated) data looks like (nor what kind of statistic you are looking at; mean, median, trend ect).

Lastly Model validation (checking if your model is “successful”) again often implies using inferential/descriptive statistics. You can think of correlating actual data to simulated data, but again the choice of what to do boils down to how your data looks like.

You can use normal inferential/descriptive statistics to test your simulation results. People often do this, especially when your model has stochastic components you often have no choice. I cant give you any more detailed advice as you haven’t supplied a description of how your (simulated) data looks like (nor what kind of statistic you are looking at; mean, median, trend ect).

Lastly Model validation (checking if your model is “successful”) again often implies using inferential/descriptive statistics. You can think of correlating actual data to simulated data, but again the choice of what to do boils down to how your data looks like.

One option you can do is run more simulations,

however another popular measure that you can use with only a few points (2 or more) is Keyfitz’s Delta (1968). It’s an (accepted) method (especially in population modeling) of quantifying the distance between two vectors:

Delta(observerd,simulated) = ½ * sum of all: abs(observed - simulated)

It's actually a standard measure of the distance between probability vectors, when used on prob. vectors the maximum distance is 1 and the minimum 0.

Now you will know best if it is suited for you.

The ref is: Keyfitz, N. 1968, Introduction to the mathematics of population. Addison-Wesley, Reading, Massachusetts, USA.

Thank you very much, TheEcologist for your input. I will do the Keyfitz's delta test for my model. Are there any other tests that might be applicable to my situation, like Tukey's test or F test?? Thanks again.

Are the only statistics you have the ones you supplied in this post or are they calculated from a set of data per storm/simulation (e.g. are the 3 pairs of datapoints means or any other summary statistic)?

TheEcologist, thanks again for your reply. These flow rates were simulated by the model for the individual storms, and they are not summary statistic like means. I know, many of the statistical tests will not be applicable here. I just wanted to make sure.

100*(sqrt((simulated-observed)^2)/observerd)

Simple is often better you know.