Title: Rainfall runoff prediction via a hybrid model of neighbourhood rough set with LSTM

Authors: Xiaoli Li; Guomei Song; Shuailing Zhou; Yujia Yan; Zhenlong Du

Addresses: School of Computer Science and Technology, Nanjing TECH University, Nanjing, 211816, China ' School of Computer Science and Technology, Nanjing TECH University, Nanjing, 211816, China ' School of Computer Science and Technology, Nanjing TECH University, Nanjing, 211816, China ' Electrical and Computer Engineering, University of Rochester, Rochester, 14620, NY, USA ' School of Computer Science and Technology, Nanjing TECH University, Nanjing, 211816, China

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.

Keywords: neighbourhood rough sets; attribute reduction; rainfall and runoff prediction; long short-term memory network.

DOI: 10.1504/IJES.2020.110654

International Journal of Embedded Systems, 2020 Vol.13 No.4, pp.405 - 413

Received: 14 Mar 2019
Accepted: 15 Aug 2019

Published online: 10 Aug 2020 *

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