EGU22-10803
https://doi.org/10.5194/egusphere-egu22-10803
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Sensitivity analysis of network structure in missing streamflow data complementation using Bidirectional Short-Term Memory network

Takeyoshi Nagasato1, Kei Ishida2, Daiju Sakaguchi1, Motoki Amagasaki3, and Masato Kiyama3
Takeyoshi Nagasato et al.
  • 1Graduate School of Science and Technology, Kumamoto University, Kumamoto, Japan
  • 2International Research Organization for Advanced Science and Technology, Kumamoto University, Kumamoto, Japan
  • 3Faculty of Advanced Science and Technology, Kumamoto University, Kumamoto, Japan

Streamflow data based on the observation may be partially missing due to flood or malfunction of the measuring equipment. Here, it is important to complement the missing flow rate with high accuracy for water resource management and flood risk management. Various statistical approaches such as linear regression and multiple regression models have been proposed as methods for complementing missing flow rates. Among the statistical methods, deep learning has been rapidly evolved with the improvement of computational equipment. Then, deep learning methods have achieved remarkable success in various fields. It may indicate that there is a possibility that the missing flow rate can be complemented with high accuracy by using the deep learning method. Therefore, this study implemented deep learning for missing streamflow complementation. In addition, because the network structure of deep learning may have a great influence on estimation accuracy, this study conducted a sensitivity analysis of the network structure. Among the deep learning methods, Bidirectional LSTM (Bi-LSTM) was implemented in this study. Bi-LSTM is a kind of LSTM that can learn long-term dependence of time series data. Bi-LSTM learns data in both forward and backward directions, compared to Unidirectional LSTM which learns data forward directions. As for the input data, both hourly streamflow and precipitation data were used. For model learning and evaluation, missing streamflow data were artificially generated. The results show that Bi-LSTM can complement the flow rate with high accuracy. It also showed the importance of optimizing the network structure.

How to cite: Nagasato, T., Ishida, K., Sakaguchi, D., Amagasaki, M., and Kiyama, M.: Sensitivity analysis of network structure in missing streamflow data complementation using Bidirectional Short-Term Memory network, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10803, https://doi.org/10.5194/egusphere-egu22-10803, 2022.

Comments on the display material

to access the discussion