Multi-step-ahead prediction of river flow using NARX neural networks and deep learning LSTM. Issue 1 (25th January 2022) - Hayder, Gasim; Iwan Solihin, Mahmud; Najwa, M. R. N.
Abstract: Kelantan river (Sungai Kelantan in Malaysia) basin is one of the essential catchments as it has a history of flood events. Numerous studies have been conducted in river basin modelling for the prediction of flow and mitigation of flooding events as well as water resource management. Therefore, having multi-step-ahead forecasting for river flow (RF) is of important research interest in this regard. This study presents four different approaches for multi-step-ahead forecasting for the Kelantan RF, using NARX (nonlinear autoregressive with exogenous inputs) neural networks and deep learning recurrent neural networks called LSTM (long short-term memory). The dataset used was obtained in monthly record for 29 years between January 1988 and December 2016. The results show that two recursive methods using NARX and LSTM are able to do multi-step-ahead forecasting on 52 series of test datasets with NSE (Nash–Sutcliffe efficiency coefficient) values of 0.44 and 0.59 for NARX and LSTM, respectively. For few-step-ahead forecasting, LSTM with direct sequence-to-sequence produces promising results with a good NSE value of 0.75 (in case of two-step-ahead forecasting). However, it needs a larger data size to have better performance in longer-step-ahead forecasting. Compared with other studies, the data used in this study is much smaller. HIGHLIGHTS: Four different approaches for multi-step-ahead forecasting for river flow. Two forecasting approaches are performed in multivariate approach using NARXNN (nonlinear autoregressive with exogenous inputs - neural networks) with respectively recursive and direct auto-regressive mode. Two forecasting approaches are performed using LSTM (long short-term memory) with respectively recursive univariate and sequence-to-sequence multivariate approach. LSTM with a direct sequence-to-sequence multivariate approach performs the best for few-step-ahead forecasting in the limited data size. It has promising application for longer-step-ahead forecasting provided that the data size is sufficiently large. Graphical Abstract