Sentence examples for lead time predictions from inspiring English sources

If we compare long lead time predictions of Tables 2 and 3 with corresponding Tables 4 and 5 we find that the stand alone networks exhibit better performance at higher lead times than the combined numerical-neural method.

Overall, superiority of the present approach especially for a longer (6-h) lead time prediction is revealed through a systematic comparison among three individual methods (i.e., the physically based hydrologic model, artificial neural network, and support vector machine) as well as their two hybrid combinations.

Similar to site 1 although the effect of data assimilation reduced with lead time the predictions were fairly good till five time steps ahead.

It may be noticed that although the method's performance reduced with lead time the predictions were fairly good till five time steps ahead.

Therefore, W-SAS-MP can be an appropriate tool for enhancing daily rainfall prediction accuracy and extend prediction lead time.

In this study, two methods called combined season-multilayer perceptron (SAS-MP) and hybrid wavelet-season-multilayer perceptron (W-SAS-MP) were developed to enhance prediction accuracy and extend prediction lead time of daily rainfall up to 5 days by using data from two stations in Turkey.

Throughout all the lead times, however, the hybrid W-SAS-MP model performed best with CE values of 0.911 and 0.909, respectively, for prediction lead time of 1 day and 0.588 and 0.570, respectively, for prediction lead time of 5 days at Stations 17836 and 17837, respectively, at the model testing (validation) phase.

However, because of its nonlinearity, prediction of daily rainfall with high accuracy and long prediction lead time is difficult.

The accuracy of radar-based rainfall prediction performs best for very short lead time, however the accuracy of radar prediction rapidly decreases with increasing lead times.

There are many daily rainfall prediction methods in the literature, but they are known to yield inaccurate predictions with short lead time, require many physical parameters and involve complicated mathematical equations with huge computational burden.

But still, the accuracy has not been satisfactory and predictions are with short lead time.