Sentence examples for monthly streamflow forecasts from inspiring English sources

Monthly streamflow forecasts with long lead time are being sought by water managers in Australia.

Monthly streamflow forecasts are needed to support water resources decision making in the South East of South Australia, where baseflow represents a significant proportion of the total streamflow and soil moisture and groundwater are important predictors of runoff.

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

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

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

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

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

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

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.