Runoff Prediction Based on Deep Belief Networks

Abstract


 
 
 Runoff prediction has recently become an essential task with respect to assessing the impact of climate change to people’s livelihoods and production. However, the runoff time series always exhibits nonlinear and non-stationary features, which makes it very difficult to be accurately predicted. Machine learning have been recently proved to be a powerful tool in helping society adapt to a changing climate and its subfield, deep learning, showed the power in approximate nonlinear functions. In this study, we propose a method based on deep belief networks (DBN) for runoff prediction. In order to evaluate the proposed method, we collected runoff datasets from Srepok and Dak Nong rivers located in mountain regions of the Central Highland of Vietnam in the periods of 2001-2007 at Dak Nong hydrology station and 1990-2011 at Buon Don hydrology station, respectively. Experimental results show that DBN outperforms, respectively, LSTM, BiLSTM, multi-layer perceptron (MLP) trained by particle swarm optimization (PSO) and MLP trained by stochastic gradient descent (SGD) in which gradients are computed using the backpropagation (BP) procedure. The results also confirm that DBN is suitable to employ for the task of runoff prediction.