Rainfall runoff prediction via a hybrid model of neighbourhood rough set with LSTM Online publication date: Tue, 27-Oct-2020
by Xiaoli Li; Guomei Song; Shuailing Zhou; Yujia Yan; Zhenlong Du
International Journal of Embedded Systems (IJES), Vol. 13, No. 4, 2020
Abstract: Accurate rainfall runoff prediction is crucial for flood forecasting and water resources management, and it remains a challenging issue in hydrological information processing. The most challenging problem is that the hydrological information has a strong locality and nonlinearity, which leads to poor prediction accuracy. Neighbourhood rough sets has strong capability on data classification and reduction, which can reduce those redundant rainfall runoff data. Long short-term memory (LSTM) network is a special recurrent neural network (RNN) that is an excellent variant of RNN, it is good at handling the time series data. In the paper, a hybrid model of neighbourhood rough sets with LSTM is proposed, which is used for the rainfall runoff prediction. The experimental results show that the presented model could improve the training speed of LSTM and achieve much higher prediction accuracy than the conventional rainfall runoff prediction methods.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Embedded Systems (IJES):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook