论文标题
基于LSTM的小河流的河流预测
Stream-Flow Forecasting of Small Rivers Based on LSTM
论文作者
论文摘要
小河的河流预测始终非常重要，但由于体积较小的河流的特殊特征，却相对挑战。人工智能（AI）方法已在该领域长期使用，但预测质量的提高仍在进行中。在本文中，我们试图提供一种新方法来使用长期术语内存（LSTM）深度学习模型进行预测，该模型的目标是时间序列数据。利用LSTM，我们从中国Tunxi的一个水文站收集了流流数据，以及来自11个降雨站的降水数据，以预测将来6小时的该水文站的流流数据。我们使用三个标准评估了预测结果：均方根误差（RMSE），平均绝对误差（MAE）和确定系数（R^2）。通过比较LSTM的预测与支持矢量回归（SVR）和多层感知（MLP）模型的预测，我们表明LSTM具有更好的性能，可实现82.007的RMSE，MAE为27.752，为0.970的R^2。我们还对LSTM模型进行了扩展实验，讨论了其性能的影响因素。
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.
