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Generally hydrologist rely heavily on the nash sutcliffe model efficiency other wise known as r2 or the coefficient of determination or at least one of them.
Nash sutcliffe efficiency matlab. With the nash sutcliffe measure an r square coefficient is calculated using coefficient values equal to 1 indicate a perfect fit between observed and predicted data and r square values equal to or less than 0 indicate that the model is predicting no better than using the average of the observed data. Usually this folder can be cusersyouruserdocumentsmatlab or any folder that you selected. E 1 y i y i s i m 2 y i y 2 the variables are the same as described above but y i s i m are the predictions from the simulation instead of the y i from a statistical model. 1 using matlab code for calculation nash sutcliffe model efficiency coefficient.
For this purpose firstly you should download nsem file and copy it in matlab workfolder. Sum qobs qmean2. In the situation of a perfect model with an estimation error variance equal to zero the resulting nash sutcliffe efficiency equals 1 nse 1. The nash sutcliffe coefficient is calculated as.
This gives an aggregated measure. The nashsutcliffe efficiency is calculated as one minus the ratio of the error variance of the modeled time series divided by the variance of the observed time series. We have two methods for calculation nash sutcliffe model efficiency coefficient on our website. The nash sutcliffe model efficiency coefficient e is used to quantify how well a model simulation can predict the outcome variable.
Qobs qsim2 1 qobs qmean2 with the nash sutcliffe measure an r square coefficient is calculated using coefficient values equal to 1 indicate a perfect fit between observed and predicted data and r square values equal to or less than 0 indicate that the model is predicting no better than using the average of the observed data. Nash sutcliffe efficiency indicates how well the plot of observed versus simulated data fits the 11 line. The nash sutcliffe coefficient is calculated as. The nash sutcliffe efficiency nse is a normalized statistic that determines the relative magnitude of the residual variance compared to the measured data variance nash and sutcliffe 1970.
Ns 1.
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