4.6 Article

Comparison of Long Short Term Memory Networks and the Hydrological Model in Runoff Simulation

Journal

WATER
Volume 12, Issue 1, Pages -

Publisher

MDPI
DOI: 10.3390/w12010175

Keywords

LSTM; runoff simulation; Poyang Lake Basin; deep learning

Funding

  1. National Key Research and Development Program of China [2018YFE0206400, 2018YFC0407606]
  2. National Natural Science Foundation of China [41971137, 41771235]
  3. STS Key Projects of the Chinese Academy of Sciences [KFJ-STS-QYZD-098]
  4. Science and Technology Planning Project of Qinghai Province [2019-HZ-818]
  5. China Three Gorges Corporation [01903145]
  6. Nanjing Institute of Geography Limnology [NIGLAS2019QD005]
  7. Sichuan Provincial Science and Technology Department [2018GZ0499]
  8. Education Department of Sichuan Province [17ZB0399]

Ask authors/readers for more resources

Runoff modeling is one of the key challenges in the field of hydrology. Various approaches exist, ranging from physically based over conceptual to fully data driven models. In this paper, we propose a data driven approach using the state-of-the-art Long-Short-Term-Memory (LSTM) network. The proposed model was applied in the Poyang Lake Basin (PYLB) and its performance was compared with an Artificial Neural Network (ANN) and the Soil & Water Assessment Tool (SWAT). We first tested the impacts of the number of previous time step (window size) in simulation accuracy. Results showed that a window in improper large size will dramatically deteriorate the model performance. In terms of PYLB, a window size of 15 days might be appropriate for both accuracy and computational efficiency. We then trained the model with 2 different input datasets, namely, dataset with precipitation only and dataset with all available meteorological variables. Results demonstrate that although LSTM with precipitation data as the only input can achieve desirable results (where the NSE ranged from 0.60 to 0.92 for the test period), the performance can be improved simply by feeding the model with more meteorological variables (where NSE ranged from 0.74 to 0.94 for the test period). Moreover, the comparison results with the ANN and the SWAT showed that the ANN can get comparable performance with the SWAT in most cases whereas the performance of LSTM is much better. The results of this study underline the potential of the LSTM for runoff modeling especially for areas where detailed topographical data are not available.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available