Stream-Flow Forecasting of Small Rivers Based on LSTM
This work addresses forecasting challenges for small rivers, which is important for hydrology and water management, but it is incremental as it applies an existing LSTM method to a specific dataset.
The paper tackled stream-flow forecasting for small rivers by applying an LSTM model to predict flow 6 hours ahead using data from a hydrologic station and 11 rainfall stations, achieving an RMSE of 82.007, MAE of 27.752, and R^2 of 0.970, which outperformed SVR and MLP models.
Stream-flow forecasting for small rivers has always been of great importance, yet comparatively challenging due to the special features of rivers with smaller volume. Artificial Intelligence (AI) methods have been employed in this area for long, but improvement of forecast quality is still on the way. In this paper, we tried to provide a new method to do the forecast using the Long-Short Term Memory (LSTM) deep learning model, which aims in the field of time-series data. Utilizing LSTM, we collected the stream flow data from one hydrologic station in Tunxi, China, and precipitation data from 11 rainfall stations around to forecast the stream flow data from that hydrologic station 6 hours in the future. We evaluated the prediction results using three criteria: root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R^2). By comparing LSTM's prediction with predictions of Support Vector Regression (SVR) and Multilayer Perceptions (MLP) models, we showed that LSTM has better performance, achieving RMSE of 82.007, MAE of 27.752, and R^2 of 0.970. We also did extended experiments on LSTM model, discussing influence factors of its performance.