Sentence examples for monthly streamflow prediction from inspiring English sources

However, model accuracy in monthly streamflow prediction is higher with seasonal differencing than with the other two methods.

Thus, to optimize the prediction accuracy of CART for monthly streamflow forecasting, we incorporate bagging and stochastic gradient boosting which are rooted in same philosophy, advancing the prediction accuracy of weak learners.

Overall, it is found that ensemble learning paradigms can remarkably advance the prediction accuracy of CART models in monthly streamflow forecasting.

This study develops configurational entropy theory (CET) for monthly streamflow forecasting.

Then this approach is tested on a typical empirical hydrological model for monthly streamflow forecasting.

The present study proposes a new hybrid evolutionary Adaptive Neuro-Fuzzy Inference Systems (ANFIS) approach for monthly streamflow forecasting.

It can be concluded that the hybrid models are not suitable for monthly streamflow forecasting in this study.

In this study, we combined a hidden Markov model (HMM) and Gaussian Mixture Regression (GMR) for probabilistic monthly streamflow forecasting.

Comparing with the results of bagged regression trees (BRTs) and stochastic gradient boosted regression trees (GBRTs) models possess satisfactory monthly streamflow forecasting performance than CART and SVR models.

Therefore, integrating model averaging techniques with the hybrid DWT SVR model would be a promising approach for daily and monthly streamflow forecasting.

The study, initially, investigates the use of classification and regression trees for monthly streamflow forecasting and employs a support vector regression (SVR) model as the benchmark model.