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A probolem for muti-timesteps input factors using sobol sensitivity analysis #503
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Please take a look at @jdherman's paper "From maps to movies: high-resolution time-varying sensitivity analysis for spatially distributed watershed models" |
@willu47 Thank you very much for this paper. Recently I find a paper about Can I use the |
I think this is true of any SA approach that does not require a specific sample scheme. For HDMR specifically though, you may run into several practical issues. For one, HDMR seems to be a very computationally intensive approach. It can use a lot of memory, particularly for large number of dimensions. You can try and see what happens. |
Yeah, HDMR methods can't resolve the problem with more than 100 parameters and it will consume lots of memory. In the last, I group parameters into dimensions to reduce analyze time by using Morris or Sobol method. |
Hi, I have a model which can predict the water quality. But the input factors are all coninues variable(time series), such as y = f(x1, x2, x3) , and xi has the t time step input, and i belongs to [1, k]. In the other word, the model's input is a (t * k) matix. I want to analyze the contribution for every component in all t time. How I can do this?
My current idea is to treat every time_step of factors as a singe varaiable , and then sampe every varaiable independently .
Finally, group t time steps varaiable into a component or sum the every varaiable in time diemention.
My definition of
problem
is below:Is my approach feasible or reansonable? How I can count this time series input factors sensitivity?
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