Evaluation of Streamflow Forecasting Models

1994 ◽  
pp. 77-85 ◽  
Author(s):  
Tao Tao ◽  
William C. Lennox
Keyword(s):  
Streamflow Forecasting ◽  
Forecasting Models

scholarly journals Remote-Sensing-Based Streamflow Forecasting Using Artificial Neural Network and Support Vector Machine Models

2021 ◽  
Vol 13 (20) ◽  
pp. 4147
Author(s):  
Mohammed M. Alquraish ◽  
Mosaad Khadr
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Superior Performance ◽  
Support Vector ◽  
Streamflow Forecasting ◽  
Forecasting Models ◽  
Monthly Streamflow ◽  
Artificial Neural ◽  
Monthly Streamflow Forecasting

In this study, we aimed to investigate the hydrological performance of three gridded precipitation products—CHIRPS, RFE, and TRMM3B42V7—in monthly streamflow forecasting. After statistical evaluation, two monthly streamflow forecasting models—support vector machine (SVM) and artificial neural network (ANN)—were developed using the monthly temporal resolution data derived from these products. The hydrological performance of the developed forecasting models was then evaluated using several statistical indices, including NSE, MAE, RMSE, and R2. The performance measures confirmed that the CHIRPS product has superior performance compared to RFE 2.0 and TRMM data, and it could provide reliable rainfall estimates for use as input in forecasting models. Likewise, the results of the forecasting models confirmed that the ANN and SVM both achieved acceptable levels of accuracy for forecasting streamflow; however, the ANN model was superior (R2 = 0.898–0.735) to the SVM (R2 = 0.742–0.635) in both the training and testing periods.

Examining the applicability of different sampling techniques in the development of decomposition-based streamflow forecasting models

2019 ◽  
Vol 568 ◽  
pp. 534-550 ◽  
Author(s):  
Wei Fang ◽  
Shengzhi Huang ◽  
Kun Ren ◽  
Qiang Huang ◽  
Guohe Huang ◽  
...  
Keyword(s):  
Sampling Techniques ◽  
Streamflow Forecasting ◽  
Forecasting Models

MACHINE LEARNING TECHNIQUES APPLIED TO ITAIPU STREAMFLOW FORECASTING

2020 ◽  
Author(s):  
Jorge Gustavo Sandoval Simão ◽  
Gabriel Ribeiro ◽  
Viviana Mariani ◽  
Leandro Coelho
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Streamflow Forecasting ◽  
Learning Techniques

A rough set theory application in forecasting models

2020 ◽  
Vol 3 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Haresh Sharma ◽  
◽  
Kriti Kumari ◽  
Samarjit Kar ◽  
◽  
...  
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Forecasting Models ◽  
Theory Application
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