Sentence examples for monthly streamflow forecasting from inspiring English sources

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.

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.

The comparison of results reveals that the M-EMDSVM approach has provided a superior alternative to ANN, SVM and EMD SVM models for forecasting monthly streamflow at Huaxian hydrological station, and its pass rate of prediction reaches up to 82.6% in Huaxian station.

In this study, two AI techniques, including hybrid wavelet-artificial neural network (WANN) and linear genetic programming (LGP) technique have been proposed to forecast monthly streamflow in a particular catchment and then performance of the proposed models were compared with each other in terms of root mean square error (RMSE) and Nash Sutcliffe efficiency (NSE) measures.

Skilful forecasting of monthly streamflow in intermittent rivers is a challenging task in stochastic hydrology.