Imperial College London
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ProfessorChristopherPain

Faculty of Engineering, Department of Earth Science & Engineering

Professorial Research Fellow
 
 
 
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Contact

 

+44 (0)20 7594 9322c.pain

 
 
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Location

 

4.96Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Cheng:2020:10.1016/j.jhydrol.2020.125376,
author = {Cheng, M and Fang, F and Kinouchi, T and Navon, IM and Pain, CC},
doi = {10.1016/j.jhydrol.2020.125376},
journal = {Journal of Hydrology},
pages = {1--13},
title = {Long lead-time daily and monthly streamflow forecasting using machine learning methods},
url = {http://dx.doi.org/10.1016/j.jhydrol.2020.125376},
volume = {590},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Long lead-time streamflow forecasting is of great significance for water resources planning and management in both the short and long terms. Despite of some studies using machine learning methods in streamflow forecasting, only few studies have been conducted to explore long lead-time forecasting capabilities of these methods, and gain an insight into systematic comparison of model forecasting performance in both the short and long terms. In this work, an artificial neural network (ANN) and a long short term memory (LSTM), a powerful tool for learning long-term temporal dependencies and capturing nonlinear relationship, have been adopted to forecast streamflow at daily and monthly scales for a long lead-time period. For long lead-time streamflow forecasting, a recursive forecasting procedure, which takes the last one-step-ahead forecast as a new input for the next-step-ahead forecast, is used in the ANN and LSTM forecasting systems. Two models are trained and validated for streamflow forecasting using the rainfall and runoff datasets collected from the Nan River Basin and Ping River Basin, Thailand, covering the period 1974 to 2014. To further explore the impact of parameter settings on model performance, two parameters, i.e. the length of time lag and the number of maximum epochs, are examined in the ANN and LSTM models. The main findings are highlighted here. First, with an optimal setting up of model parameters, both the ANN and LSTM model can provide accurate daily forecasting (up to 20 days ahead). Second, in comparison to the ANN model, the LSTM model exhibits better model performance in long lead-time daily forecasting, but less satisfactory in multi-monthly forecasting due to lack of large monthly training dataset. Third, the selection of the length of the time lag and number of maximum epochs used in both ANN and LSTM modelling are the key for long lead-time streamflow forecasting at daily and monthly scales. These findings suggest that the LSTM could be ad
AU  - Cheng,M
AU  - Fang,F
AU  - Kinouchi,T
AU  - Navon,IM
AU  - Pain,CC
DO  - 10.1016/j.jhydrol.2020.125376
EP  - 13
PY  - 2020///
SN  - 0022-1694
SP  - 1
TI  - Long lead-time daily and monthly streamflow forecasting using machine learning methods
T2  - Journal of Hydrology
UR  - http://dx.doi.org/10.1016/j.jhydrol.2020.125376
UR  - https://www.sciencedirect.com/science/article/pii/S0022169420308362?via%3Dihub
VL  - 590
ER  -
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