Deep Learning with a Long Short-Term Memory Networks Approach for Rainfall-Runoff Simulation
By Cai-hong Hu, Qiang Wu, Hui Li, S. Jian, Nan Li, Zhengzheng Lou. Water, 2018
508 citations recorded by the source corpus.
Considering the high random and non-static property of the rainfall-runoff process, lots of models are being developed in order to learn about such a complex phenomenon. Recently, Machine learning techniques such as the Artificial Neural Network (ANN) and other networks have been extensively used by hydrologists for rainfall-runoff modelling as well as for other fields of hydrology. However, deep learning methods such as the state-of-the-art for LSTM networks are little studied in hydrological sequence time-series predictions. We deployed ANN and LSTM network models for simulating the rainfall-runoff process based on flood events from 1971 to 2013 in Fen River basin monitored through 14 rainfall stations and one hydrologic station in the catchment. The experimental data were from 98 rainfall-runoff events in this period. In between 86 rainfall-runoff events were used as training set, and the rest were used as test set. The results show that the two networks are all suitable for rainfall-runoff models and better than conceptual and physical based models. LSTM models outperform the ANN models with the values of R 2 and N S E beyond 0.9, respectively. Considering different lead time modelling the LSTM model is also more stable than ANN model holding better simulation performance. The special units of forget gate makes LSTM model better simulation and more intelligent than ANN model. In this study, we want to propose new data-driven methods for flood forecasting.
Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/70fc34f1075ccb06cfd6b3f3821bf093251efb66
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In Environmental Engineering
In Global and Planetary Change
In Water Science and Technology
Citations in the corpus
- A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning (2020, methodology): “For example, Kratzert et al. (2018) used LSTM to predict runoff for the next day, and Hu et al. (2018) used LSTM to predict the runoff for the next 6 hr. Recently, Reichstein et al. (2019) proposed that language translation modeling methods could be applied to time series tasks.”
- Enhancing Streamflow Forecast and Extracting Insights Using Long‐Short Term Memory Networks With Data Integration at Continental Scales (2019, background): “Similarly, to predict streamflow at one gage, Hu et al. (2018) and Le et al. (2019) inserted streamflow observations an hour or a day before the prediction to improve LSTM forecast, which showed promising results.”