Improving event-based rainfall-runoff simulation using an ensemble artificial neural network based hybrid data-driven model

2015 ◽  
Vol 29 (5) ◽  
pp. 1345-1370 ◽  
Author(s):  
Guangyuan Kan ◽  
Cheng Yao ◽  
Qiaoling Li ◽  
Zhijia Li ◽  
Zhongbo Yu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Driven ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Hybrid Data ◽  
Artificial Neural ◽  
Event Based

scholarly journals The utilization of a GR4J model and wavelet-based artificial neural network for rainfall–runoff modelling

2018 ◽  
Vol 19 (5) ◽  
pp. 1295-1304
Author(s):  
C. Sezen ◽  
T. Partal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Potential Evapotranspiration ◽  
Data Driven ◽  
Rainfall Runoff ◽  
Ann Model ◽  
The Usa ◽  
Streamflow Data ◽  
Artificial Neural ◽  
Better Than

Abstract Data-driven models and conceptual models have been utilized in an attempt to perform rainfall–runoff modelling. The aim of this study is comparing the performance of an artificial neural network (ANN) model, wavelet-based artificial neural network (WANN) model and GR4J lumped daily conceptual model for rainfall–runoff modelling of two rivers in the USA. It was obtained that the performance of the data-driven models (ANN, WANN) is better than the GR4J model especially when streamflow data the preceding day (Qt-1) and streamflow data the preceding two days (Qt-2) are used as input data in the ANN and WANN models for the simulation of low and high flows, in particular. On the other hand, when only precipitation and potential evapotranspiration data are used as input variables, the GR4J model performs better than the data-driven models.

Applying the ensemble artificial neural network-based hybrid data-driven model to daily total load forecasting

2014 ◽  
Vol 26 (3) ◽  
pp. 603-611 ◽  
Author(s):  
Jia-rui Dong ◽  
Chui-yong Zheng ◽  
Guang-yuan Kan ◽  
Min Zhao ◽  
Jie Wen ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Load Forecasting ◽  
Data Driven ◽  
Total Load ◽  
Hybrid Data ◽  
Artificial Neural ◽  
Daily Total

A new hybrid data-driven model for event-based rainfall–runoff simulation

2016 ◽  
Vol 28 (9) ◽  
pp. 2519-2534 ◽  
Author(s):  
Guangyuan Kan ◽  
Jiren Li ◽  
Xingnan Zhang ◽  
Liuqian Ding ◽  
Xiaoyan He ◽  
...  
Keyword(s):  
Data Driven ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Hybrid Data ◽  
Event Based

Copula entropy coupled with artificial neural network for rainfall–runoff simulation

2013 ◽  
Vol 28 (7) ◽  
pp. 1755-1767 ◽  
Author(s):  
Lu Chen ◽  
Vijay P. Singh ◽  
Shenglian Guo ◽  
Jianzhong Zhou ◽  
Lei Ye
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Artificial Neural

A data-driven algorithm for constructing artificial neural network rainfall-runoff models

2002 ◽  
Vol 16 (6) ◽  
pp. 1325-1330 ◽  
Author(s):  
K. P. Sudheer ◽  
A. K. Gosain ◽  
K. S. Ramasastri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Driven ◽  
Rainfall Runoff ◽  
Artificial Neural

Feed-forward artificial neural network provides data-driven inference of functional connectivity

2019 ◽  
Vol 29 (9) ◽  
pp. 091101 ◽  
Author(s):  
Nikita Frolov ◽  
Vladimir Maksimenko ◽  
Annika Lüttjohann ◽  
Alexey Koronovskii ◽  
Alexander Hramov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Functional Connectivity ◽  
Data Driven ◽  
Feed Forward ◽  
Artificial Neural
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