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Open AccessArticle

Deep Learning with a Long Short-Term Memory Networks Approach for Rainfall-Runoff Simulation

1
School of Water Conservancy and Environment, Zhengzhou University, Zhengzhou 450001, China
2
School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China
*
Author to whom correspondence should be addressed.
Water 2018, 10(11), 1543; https://doi.org/10.3390/w10111543
Received: 31 August 2018 / Revised: 19 October 2018 / Accepted: 25 October 2018 / Published: 30 October 2018
(This article belongs to the Special Issue Flood Forecasting Using Machine Learning Methods)
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. View Full-Text
Keywords: LSTM; rainfall-runoff; flood events LSTM; rainfall-runoff; flood events
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Hu, C.; Wu, Q.; Li, H.; Jian, S.; Li, N.; Lou, Z. Deep Learning with a Long Short-Term Memory Networks Approach for Rainfall-Runoff Simulation. Water 2018, 10, 1543.

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